
@article{kahle_preserving_1997,
	title = {{PRESERVING} {THE} {INTERNET}},
	volume = {276},
	issn = {0036-8733},
	url = {https://www.jstor.org/stable/24993660},
	number = {3},
	urldate = {2019-09-22},
	journal = {Scientific American},
	author = {Kahle, Brewster},
	year = {1997},
	pages = {82--83}
}

@inproceedings{salaheldeen_losing_2012,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Losing {My} {Revolution}: {How} {Many} {Resources} {Shared} on {Social} {Media} {Have} {Been} {Lost}?},
	isbn = {978-3-642-33290-6},
	shorttitle = {Losing {My} {Revolution}},
	abstract = {Social media content has grown exponentially in the recent years and the role of social media has evolved from just narrating life events to actually shaping them. In this paper we explore how many resources shared in social media are still available on the live web or in public web archives. By analyzing six different event-centric datasets of resources shared in social media in the period from June 2009 to March 2012, we found about 11\% lost and 20\% archived after just a year and an average of 27\% lost and 41\% archived after two and a half years. Furthermore, we found a nearly linear relationship between time of sharing of the resource and the percentage lost, with a slightly less linear relationship between time of sharing and archiving coverage of the resource. From this model we conclude that after the first year of publishing, nearly 11\% of shared resources will be lost and after that we will continue to lose 0.02\% per day.},
	language = {en},
	booktitle = {Theory and {Practice} of {Digital} {Libraries}},
	publisher = {Springer Berlin Heidelberg},
	author = {SalahEldeen, Hany M. and Nelson, Michael L.},
	editor = {Zaphiris, Panayiotis and Buchanan, George and Rasmussen, Edie and Loizides, Fernando},
	year = {2012},
	keywords = {Digital Preservation, Social Media, Web Archiving},
	pages = {125--137}
}

@misc{noauthor_whats_nodate,
	title = {What’s the average lifespan of a {Web} page? {\textbar} {JISC} {PoWR}},
	shorttitle = {What’s the average lifespan of a {Web} page?},
	url = {https://jiscpowr.jiscinvolve.org/wp/2009/08/12/whats-the-average-lifespan-of-a-web-page/},
	language = {en-US},
	urldate = {2019-09-22}
}

@misc{noauthor_whats_nodate-1,
	title = {What’s the average lifespan of a {Web} page? {\textbar} {JISC} {PoWR}},
	shorttitle = {What’s the average lifespan of a {Web} page?},
	url = {https://jiscpowr.jiscinvolve.org/wp/2009/08/12/whats-the-average-lifespan-of-a-web-page/},
	language = {en-US},
	urldate = {2019-09-22}
}

@article{castillo_effective_2005,
	title = {Effective {Web} {Crawling}},
	volume = {39},
	issn = {0163-5840},
	url = {http://doi.acm.org/10.1145/1067268.1067287},
	doi = {10.1145/1067268.1067287},
	abstract = {The key factors for the success of the World Wide Web are its large size and the lack of a centralized control over its contents. Both issues are also the most important source of problems for locating information. The Web is a context in which traditional Information Retrieval methods are challenged, and given the volume of the Web and its speed of change, the coverage of modern search engines is relatively small. Moreover, the distribution of quality is very skewed, and interesting pages are scarce in comparison with the rest of the content.},
	number = {1},
	urldate = {2019-09-22},
	journal = {SIGIR Forum},
	author = {Castillo, Carlos},
	month = jun,
	year = {2005},
	pages = {55--56}
}

@inproceedings{radinsky_predicting_2013,
	address = {New York, NY, USA},
	series = {{WSDM} '13},
	title = {Predicting {Content} {Change} on the {Web}},
	isbn = {978-1-4503-1869-3},
	url = {http://doi.acm.org/10.1145/2433396.2433448},
	doi = {10.1145/2433396.2433448},
	abstract = {Accurate prediction of changing web page content improves a variety of retrieval and web related components. For example, given such a prediction algorithm one can both design a better crawling strategy that only recrawls pages when necessary as well as a proactive mechanism for personalization that pushes content associated with user revisitation directly to the user. While many techniques for modeling change have focused simply on past change frequency, our work goes beyond that by additionally studying the usefulness in page change prediction of: the page's content; the degree and relationship among the prediction page's observed changes; the relatedness to other pages and the similarity in the types of changes they undergo. We present an expert prediction framework that incorporates the information from these other signals more effectively than standard ensemble or basic relational learning techniques. In an empirical analysis, we find that using page content as well as related pages significantly improves prediction accuracy and compare it to common approaches. We present numerous similarity metrics to identify related pages and focus specifically on measures of temporal content similarity. We observe that the different metrics yield related pages that are qualitatively different in nature and have different effects on the prediction performance.},
	urldate = {2019-09-22},
	booktitle = {Proceedings of the {Sixth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}},
	publisher = {ACM},
	author = {Radinsky, Kira and Bennett, Paul N.},
	year = {2013},
	note = {event-place: Rome, Italy},
	keywords = {web change prediction, web crawling, web dynamics},
	pages = {415--424}
}

@inproceedings{meegahapola_detection_2017,
	title = {Detection of change frequency in web pages to optimize server-based scheduling},
	doi = {10.1109/ICTER.2017.8257791},
	abstract = {The Internet at present has become vast and dynamic with the ever increasing number of web pages. These web pages change when more content is added to them. With the availability of change detection and notification systems, keeping track of the changes occurring in web pages has become more simple and straightforward. However, most of these change detection and notification systems work based on predefined crawling schedules with static time intervals. This can become inefficient if there are no relevant changes being made to the web pages, resulting in the wastage of both temporal and computational resources. If the web pages are not crawled frequently, some of the important changes may be missed and there may be delays in notifying the subscribed users. This paper proposes a methodology to detect the frequency of change in web pages to optimize server-side scheduling of change detection and notification systems. The proposed method is based on a dynamic detection process, where the crawling schedule will be adjusted accordingly in order to result in a more efficient server-based scheduler to detect changes in web pages.},
	booktitle = {2017 {Seventeenth} {International} {Conference} on {Advances} in {ICT} for {Emerging} {Regions} ({ICTer})},
	author = {Meegahapola, L. and Alwis, R. and Nimalarathna, E. and Mallawaarachchi, V. and Meedeniya, D. and Jayarathna, S.},
	month = sep,
	year = {2017},
	keywords = {Algorithm design and analysis, Crawlers, Internet, Monitoring, Schedules, Servers, Web pages, change detection and notification systems, change frequency, change frequency detection, crawling, notification systems, scheduling, server-based scheduling, server-side scheduling, static time intervals, web page, web pages},
	pages = {1--7}
}

@patent{auerbach_scheduling_2013,
	title = {Scheduling a recrawl},
	url = {https://patents.google.com/patent/US8386459B1/en},
	nationality = {US},
	assignee = {Google LLC},
	number = {US8386459B1},
	urldate = {2019-09-10},
	author = {Auerbach, David B. and Alpert, Jesse L.},
	month = feb,
	year = {2013},
	keywords = {crawl, item, items, period, recrawl}
}

@article{brawer_54_nodate,
	title = {(54) {WEB} {CRAWLERSCHEDULER} {THAT} {UTILIZES} {STEMAPS} {FROMWEBSITES}},
	abstract = {Methods and systems for a web crawler scheduler that utilizes sitemaps from websites are described. A web crawler sched uling system receives a notification from a website or web server. In response to the notification, the system accesses one or more sitemap(s) for documents associated with the website or web server. The system schedules crawls of the documents based on information identified from the sitemaps. The sys tem crawls at least a subset of the documents scheduled for crawling.},
	language = {en},
	author = {Brawer, Sascha B and Ibel, Maximilian and Keller, Ralph Michael and Shivakumar, Narayanan and To, Stark},
	pages = {18}
}

@patent{larsson_dynamically_2009,
	title = {Dynamically constrained, forward scheduling over uncertain workloads},
	url = {https://patents.google.com/patent/US20090077198A1/en},
	nationality = {US},
	assignee = {Daniel Mattias Larsson, Ashwinder Ahluwalia, Sridharan Gopal Krishnan},
	number = {US20090077198A1},
	urldate = {2019-09-10},
	author = {Larsson, Daniel Mattias and Ahluwalia, Ashwinder and Krishnan, Sridharan Gopal},
	month = mar,
	year = {2009},
	keywords = {download, host, item, items, searchable}
}

@patent{najork_system_2006,
	title = {System and method for distributed web crawling},
	url = {https://patents.google.com/patent/US7139747B1/en},
	nationality = {US},
	assignee = {Hewlett Packard Development Co LP},
	number = {US7139747B1},
	urldate = {2019-09-10},
	author = {Najork, Marc Alexander},
	month = nov,
	year = {2006},
	keywords = {address, identifier, url, web, web crawler}
}

@article{serrano_is_2019,
	title = {Is {Attention} {Interpretable}?},
	url = {http://arxiv.org/abs/1906.03731},
	abstract = {Attention mechanisms have recently boosted performance on a range of NLP tasks. Because attention layers explicitly weight input components' representations, it is also often assumed that attention can be used to identify information that models found important (e.g., specific contextualized word tokens). We test whether that assumption holds by manipulating attention weights in already-trained text classification models and analyzing the resulting differences in their predictions. While we observe some ways in which higher attention weights correlate with greater impact on model predictions, we also find many ways in which this does not hold, i.e., where gradient-based rankings of attention weights better predict their effects than their magnitudes. We conclude that while attention noisily predicts input components' overall importance to a model, it is by no means a fail-safe indicator.},
	urldate = {2019-06-17},
	journal = {arXiv:1906.03731 [cs]},
	author = {Serrano, Sofia and Smith, Noah A.},
	month = jun,
	year = {2019},
	note = {arXiv: 1906.03731},
	keywords = {Computer Science - Computation and Language}
}

@misc{noauthor_[1906.03731]_nodate,
	title = {[1906.03731] {Is} {Attention} {Interpretable}?},
	url = {https://arxiv.org/abs/1906.03731},
	urldate = {2019-06-17}
}

@misc{noauthor_nba___nba_nodate,
	title = {{NBA}\_腾讯体育\_腾讯网\_NBA中国数字媒体独家官方合作伙伴},
	url = {https://sports.qq.com/nba/},
	urldate = {2019-06-08}
}

@inproceedings{bai_neural_2018,
	address = {New York, NY, USA},
	series = {{CIKM} '18},
	title = {Neural {Relational} {Topic} {Models} for {Scientific} {Article} {Analysis}},
	isbn = {978-1-4503-6014-2},
	url = {http://doi.acm.org/10.1145/3269206.3271696},
	doi = {10.1145/3269206.3271696},
	abstract = {Topic modelling and citation recommendation of scientific articles are important yet challenging research problems in scientific article analysis. In particular, the inference on coherent topics can be easily affected by irrelevant contents in articles. Meanwhile, the extreme sparsity of citation networks brings difficulty to a valid citation recommendation. Intuitively, articles with similar topics are more likely to cite each other, and cited articles tend to share similar themes. Motivated from this intuition, we aim to boost the performance of both topic modelling and citation recommendation by effectively leverage this underlying correlation between latent topics and citation networks. To this end, we propose a novel Bayesian deep generative model termed as Neural Relational Topic Model (NRTM), which is composed with a Stacked Variational Auto-Encoder (SVAE) and a multilayer perception (MLP). Specifically, the SVAE utilizes an inference network to learn more representative topics of document contents, which can help to enrich the latent factors in collaborative filtering of citations. Furthermore, the MLP network conducts nonlinear collaborative filtering of citations, which can further benefit the inference of topics by leveraging the knowledge of citation networks. Extensive experiments on two real-world datasets demonstrate that our model can effectively take advantages of the coherence between topic learning and citation recommendation, and significantly outperform the state-of-the-art methods on both tasks.},
	urldate = {2019-04-17},
	booktitle = {Proceedings of the 27th {ACM} {International} {Conference} on {Information} and {Knowledge} {Management}},
	publisher = {ACM},
	author = {Bai, Haoli and Chen, Zhuangbin and Lyu, Michael R. and King, Irwin and Xu, Zenglin},
	year = {2018},
	note = {event-place: Torino, Italy},
	keywords = {citation recommendation, deep learning, topic modelling},
	pages = {27--36}
}

@inproceedings{sun_multi-source_2018,
	address = {New York, NY, USA},
	series = {{CIKM} '18},
	title = {Multi-{Source} {Pointer} {Network} for {Product} {Title} {Summarization}},
	isbn = {978-1-4503-6014-2},
	url = {http://doi.acm.org/10.1145/3269206.3271722},
	doi = {10.1145/3269206.3271722},
	abstract = {In this paper, we study the product title summarization problem in E-commerce applications for display on mobile devices. Comparing with conventional sentence summarization, product title summarization has some extra and essential constraints. For example, factual errors or loss of the key information are intolerable for E-commerce applications. Therefore, we abstract two more constraints for product title summarization: (i) do not introduce irrelevant information; (ii) retain the key information (e.g., brand name and commodity name). To address these issues, we propose a novel multi-source pointer network by adding a new knowledge encoder for pointer network. The first constraint is handled by pointer mechanism. For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism. For evaluation, we build a large collection of real-world product titles along with human-written short titles. Experimental results demonstrate that our model significantly outperforms the other baselines. Finally, online deployment of our proposed model has yielded a significant business impact, as measured by the click-through rate.},
	urldate = {2019-04-17},
	booktitle = {Proceedings of the 27th {ACM} {International} {Conference} on {Information} and {Knowledge} {Management}},
	publisher = {ACM},
	author = {Sun, Fei and Jiang, Peng and Sun, Hanxiao and Pei, Changhua and Ou, Wenwu and Wang, Xiaobo},
	year = {2018},
	note = {event-place: Torino, Italy},
	keywords = {extractive summarization, pointer network, title summarization},
	pages = {7--16}
}

@inproceedings{bosc_auto-encoding_2018,
	address = {Brussels, Belgium},
	title = {Auto-{Encoding} {Dictionary} {Definitions} into {Consistent} {Word} {Embeddings}},
	url = {https://www.aclweb.org/anthology/D18-1181},
	abstract = {Monolingual dictionaries are widespread and semantically rich resources. This paper presents a simple model that learns to compute word embeddings by processing dictionary definitions and trying to reconstruct them. It exploits the inherent recursivity of dictionaries by encouraging consistency between the representations it uses as inputs and the representations it produces as outputs. The resulting embeddings are shown to capture semantic similarity better than regular distributional methods and other dictionary-based methods. In addition, our method shows strong performance when trained exclusively on dictionary data and generalizes in one shot.},
	urldate = {2019-03-28},
	booktitle = {Proceedings of the 2018 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Bosc, Tom and Vincent, Pascal},
	month = oct,
	year = {2018},
	pages = {1522--1532}
}

@inproceedings{tissier_dict2vec_2017,
	address = {Copenhagen, Denmark},
	title = {Dict2vec : {Learning} {Word} {Embeddings} using {Lexical} {Dictionaries}},
	shorttitle = {Dict2vec},
	url = {https://www.aclweb.org/anthology/D17-1024},
	doi = {10.18653/v1/D17-1024},
	abstract = {Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks. The most efficient and popular approaches learn or retrofit such representations using additional external data. Resulting embeddings are generally better than their corpus-only counterparts, although such resources cover a fraction of words in the vocabulary. In this paper, we propose a new approach, Dict2vec, based on one of the largest yet refined datasource for describing words – natural language dictionaries. Dict2vec builds new word pairs from dictionary entries so that semantically-related words are moved closer, and negative sampling filters out pairs whose words are unrelated in dictionaries. We evaluate the word representations obtained using Dict2vec on eleven datasets for the word similarity task and on four datasets for a text classification task.},
	urldate = {2019-03-28},
	booktitle = {Proceedings of the 2017 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Tissier, Julien and Gravier, Christopher and Habrard, Amaury},
	month = sep,
	year = {2017},
	pages = {254--263}
}

@article{devlin_bert:_2018,
	title = {{BERT}: {Pre}-training of {Deep} {Bidirectional} {Transformers} for {Language} {Understanding}},
	shorttitle = {{BERT}},
	url = {http://arxiv.org/abs/1810.04805},
	abstract = {We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE benchmark to 80.4\% (7.6\% absolute improvement), MultiNLI accuracy to 86.7 (5.6\% absolute improvement) and the SQuAD v1.1 question answering Test F1 to 93.2 (1.5\% absolute improvement), outperforming human performance by 2.0\%.},
	urldate = {2019-03-09},
	journal = {arXiv:1810.04805 [cs]},
	author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
	month = oct,
	year = {2018},
	note = {arXiv: 1810.04805},
	keywords = {Computer Science - Computation and Language}
}

@article{radford_language_nodate,
	title = {Language {Models} are {Unsupervised} {Multitask} {Learners}},
	abstract = {Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspeciﬁc datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset - matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underﬁts WebText. Samples from the model reﬂect these improvements and contain coherent paragraphs of text. These ﬁndings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations.},
	language = {en},
	author = {Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
	pages = {24}
}

@misc{zhao_python_2019,
	title = {A {Python} {Toolbox} for {Scalable} {Outlier} {Detection} ({Anomaly} {Detection}): yzhao062/pyod},
	copyright = {BSD-2-Clause},
	shorttitle = {A {Python} {Toolbox} for {Scalable} {Outlier} {Detection} ({Anomaly} {Detection})},
	url = {https://github.com/yzhao062/pyod},
	urldate = {2019-03-09},
	author = {Zhao, Yue},
	month = mar,
	year = {2019},
	note = {original-date: 2017-10-03T20:29:04Z}
}

@misc{noauthor_how_nodate,
	title = {How agents see things: {On} visual representations in an emergent language game},
	shorttitle = {How agents see things},
	url = {https://research.fb.com/publications/how-agents-see-things-on-visual-representations-in-an-emergent-language-game/},
	abstract = {There is growing interest in the language developed by agents interacting in emergent-communication settings. Earlier studies have focused on the agents’ symbol usage, rather than on their representation of visual input. In this paper, we consider the referential games of Lazaridou et al. (2017), and investigate the representations the agents develop during their evolving interaction.},
	language = {en-US},
	urldate = {2019-03-07},
	journal = {Facebook Research}
}

@misc{noauthor_lingvo._2019,
	title = {Lingvo. {Contribute} to tensorflow/lingvo development by creating an account on {GitHub}},
	copyright = {Apache-2.0},
	url = {https://github.com/tensorflow/lingvo},
	urldate = {2019-03-07},
	publisher = {tensorflow},
	month = mar,
	year = {2019},
	note = {original-date: 2018-07-24T22:30:28Z}
}

@inproceedings{cotterell_deep_2018,
	address = {New Orleans, Louisiana},
	title = {A {Deep} {Generative} {Model} of {Vowel} {Formant} {Typology}},
	url = {http://www.aclweb.org/anthology/N18-1004},
	abstract = {What makes some types of languages more probable than others? For instance, we know that almost all spoken languages contain the vowel phoneme /i/; why should that be? The field of linguistic typology seeks to answer these questions and, thereby, divine the mechanisms that underlie human language. In our work, we tackle the problem of vowel system typology, i.e., we propose a generative probability model of which vowels a language contains. In contrast to previous work, we work directly with the acoustic information—the first two formant values—rather than modeling discrete sets of symbols from the international phonetic alphabet. We develop a novel generative probability model and report results on over 200 languages.},
	urldate = {2019-03-07},
	booktitle = {Proceedings of the 2018 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}, {Volume} 1 ({Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Cotterell, Ryan and Eisner, Jason},
	month = jun,
	year = {2018},
	pages = {37--46}
}

@inproceedings{lample_phrase-based_2018,
	address = {Brussels, Belgium},
	title = {Phrase-{Based} \& {Neural} {Unsupervised} {Machine} {Translation}},
	url = {http://www.aclweb.org/anthology/D18-1549},
	urldate = {2019-03-07},
	booktitle = {Proceedings of the 2018 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Lample, Guillaume and Ott, Myle and Conneau, Alexis and Denoyer, Ludovic and Ranzato, Marc'Aurelio},
	month = nov,
	year = {2018},
	pages = {5039--5049}
}

@misc{noauthor_lucene_nodate,
	title = {lucene 思维导图，让搜索引擎不再难懂},
	url = {https://www.toutiao.com/i6661757158771655182/?tt_from=weixin&utm_campaign=client_share&wxshare_count=1&timestamp=1551986450&app=news_article&utm_source=weixin&iid=65384490969&utm_medium=toutiao_ios&group_id=6661757158771655182},
	urldate = {2019-03-07}
}

@article{hancock_training_2018,
	title = {Training {Classifiers} with {Natural} {Language} {Explanations}},
	url = {http://arxiv.org/abs/1805.03818},
	abstract = {Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100\${\textbackslash}times\$ faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices.},
	urldate = {2019-03-07},
	journal = {arXiv:1805.03818 [cs]},
	author = {Hancock, Braden and Varma, Paroma and Wang, Stephanie and Bringmann, Martin and Liang, Percy and Ré, Christopher},
	month = may,
	year = {2018},
	note = {arXiv: 1805.03818},
	keywords = {Computer Science - Computation and Language}
}

@misc{huyen_library_2019,
	title = {Library to scrape and clean web pages to create massive datasets.: chiphuyen/lazynlp},
	shorttitle = {Library to scrape and clean web pages to create massive datasets.},
	url = {https://github.com/chiphuyen/lazynlp},
	urldate = {2019-03-07},
	author = {Huyen, Chip},
	month = mar,
	year = {2019},
	note = {original-date: 2019-02-27T23:23:19Z}
}

@misc{noauthor_thundergbm:_2019,
	title = {{ThunderGBM}: {Fast} {GBDTs} and {Random} {Forests} on {GPUs}. {Contribute} to {Xtra}-{Computing}/thundergbm development by creating an account on {GitHub}},
	copyright = {Apache-2.0},
	shorttitle = {{ThunderGBM}},
	url = {https://github.com/Xtra-Computing/thundergbm},
	urldate = {2019-03-07},
	publisher = {Xtra Computing Group},
	month = mar,
	year = {2019},
	note = {original-date: 2016-11-11T09:58:08Z}
}

@article{zhang_fixup_2019,
	title = {Fixup {Initialization}: {Residual} {Learning} {Without} {Normalization}},
	shorttitle = {Fixup {Initialization}},
	url = {http://arxiv.org/abs/1901.09321},
	abstract = {Normalization layers are a staple in state-of-the-art deep neural network architectures. They are widely believed to stabilize training, enable higher learning rate, accelerate convergence and improve generalization, though the reason for their effectiveness is still an active research topic. In this work, we challenge the commonly-held beliefs by showing that none of the perceived benefits is unique to normalization. Specifically, we propose fixed-update initialization (Fixup), an initialization motivated by solving the exploding and vanishing gradient problem at the beginning of training via properly rescaling a standard initialization. We find training residual networks with Fixup to be as stable as training with normalization -- even for networks with 10,000 layers. Furthermore, with proper regularization, Fixup enables residual networks without normalization to achieve state-of-the-art performance in image classification and machine translation.},
	urldate = {2019-03-05},
	journal = {arXiv:1901.09321 [cs, stat]},
	author = {Zhang, Hongyi and Dauphin, Yann N. and Ma, Tengyu},
	month = jan,
	year = {2019},
	note = {arXiv: 1901.09321},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@book{mohania_data_1999,
	address = {Berlin ; New York},
	series = {Lecture notes in computer science},
	title = {Data warehousing and knowledge discovery: {First} {International} {Conference}, {DaWaK} '99, {Florence}, {Italy}, {August} 30-{September} 1, 1999: proceedings},
	isbn = {978-3-540-66458-1},
	shorttitle = {Data warehousing and knowledge discovery},
	language = {en},
	number = {1676},
	publisher = {Springer},
	editor = {Mohania, Mukesh and Tjoa, A. Min},
	year = {1999},
	keywords = {Congresses, Data warehousing, Database searching}
}

@misc{noauthor_schemas_nodate,
	title = {Schemas - schema.org},
	url = {https://schema.org/docs/schemas.html},
	urldate = {2019-02-28}
}

@article{culpepper_research_2018,
	title = {Research {Frontiers} in {Information} {Retrieval}: {Report} from the {Third} {Strategic} {Workshop} on {Information} {Retrieval} in {Lorne} ({SWIRL} 2018)},
	volume = {52},
	issn = {01635840},
	shorttitle = {Research {Frontiers} in {Information} {Retrieval}},
	url = {http://dl.acm.org/citation.cfm?doid=3274784.3274788},
	doi = {10.1145/3274784.3274788},
	abstract = {The purpose of the Strategic Workshop in Information Retrieval in Lorne is to explore the long-range issues of the Information Retrieval ﬁeld, to recognize challenges that are on – or even over – the horizon, to build consensus on some of the key challenges, and to disseminate the resulting information to the research community. The intent is that this description of open problems will help to inspire researchers and graduate students to address the questions, and will provide funding agencies data to focus and coordinate support for information retrieval research.},
	language = {en},
	number = {1},
	urldate = {2019-02-28},
	journal = {ACM SIGIR Forum},
	author = {Culpepper, J. Shane and Diaz, Fernando and Smucker, Mark D.},
	month = aug,
	year = {2018},
	pages = {34--90}
}

@article{alp_identifying_2018,
	title = {Identifying topical influencers on twitter based on user behavior and network topology},
	volume = {141},
	issn = {0950-7051},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705117305439},
	doi = {https://doi.org/10.1016/j.knosys.2017.11.021},
	abstract = {Social media web sites have become major media platforms to share personal information, news, photos, videos and more. Users can even share live streams whenever they want to reach out to many other. This prevalent usage of social media attracted companies, data scientists, and researchers who are trying to infer meaningful information from this vast amount of data. Information diffusion and maximizing the spread of words is one of the most important focus for researchers working on social media. This information can serve many purposes such as; user or content recommendation, viral marketing, and user modeling. In this research, finding topical influential/authority users on Twitter is addressed. Since Twitter is a good platform to spread knowledge as a word of mouth approach and it has many more public profiles than protected ones, it is a target media for marketers. In this paper, we introduce a novel methodology, called Personalized PageRank, that integrates both the information obtained from network topology and the information obtained from user actions and activities in Twitter. The proposed approach aims to determine the topical influencers who are experts on a specific topic. Experimental results on a large dataset consisting of Turkish tweets show that using user specific features like topical focus rate, activeness, authenticity and speed of getting reaction on specific topics positively affects identifying influencers and lead to higher information diffusion. Algorithms are implemented on a distributed computing environment which makes high-cost graph processing more efficient.},
	journal = {Knowledge-Based Systems},
	author = {Alp, Zeynep Zengin and Öğüdücü, Şule Gündüz},
	year = {2018},
	keywords = {Evaluation metrics, LDA, PageRank, Social influence, Topic modeling, Topical influence, Twitter, User modeling, Viral marketing},
	pages = {211 -- 221}
}

@article{chen_home_2016,
	title = {Home location profiling for users in social media},
	volume = {53},
	issn = {0378-7206},
	url = {http://www.sciencedirect.com/science/article/pii/S0378720615001068},
	doi = {https://doi.org/10.1016/j.im.2015.09.008},
	abstract = {In this paper, we focus on the problem of estimating the home locations of users in the Twitter network. We propose a Social Tie Factor Graph (STFG) model to estimate a Twitter user's city-level location based on the user's following network, user-centric data, and tie strength. In STFG, relationships between users and locations are modeled as nodes, while attributes and correlations are modeled as factors. An efficient algorithm is proposed to learn model parameters and predict unknown relationships. We evaluate our proposed method by investigating Twitter networks. The experimental results demonstrate that our proposed method significantly outperforms several state-of-the-art methods.},
	number = {1},
	journal = {Information \& Management},
	author = {Chen, Jinpeng and Liu, Yu and Zou, Ming},
	year = {2016},
	keywords = {Home location, Labeled relationship, Social network, Social tie, Tie strength, Twitter},
	pages = {135 -- 143}
}

@article{alp_influence_2019,
	title = {Influence {Factorization} for identifying authorities in {Twitter}},
	volume = {163},
	issn = {0950-7051},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705118305069},
	doi = {https://doi.org/10.1016/j.knosys.2018.10.020},
	abstract = {Prevalent usage of social media attracted companies and researchers to analyze its dynamics and effects on user behavior. One of the most intriguing aspects of social networks is to identify influencers who are experts on a specific topic. With the identification of these users within the network, many applications can be built for user recommendation, information diffusion modeling, viral marketing, user modeling and many more. In this paper, we aim to identify topic-based experts using a large dataset collected from Twitter. Our proposed approach has three phases: (1) identification of topics on social media posts (more specifically, tweets), (2) user modeling, based on a group of user specific features, and (3) Influence Factorization to identify topical influencers. The main advantage of the proposed method is to identify future influencers as well as current ones on Twitter. Moreover, it is an easy to implement algorithm using Spark MLlib, which can be easily extended to include other user specific features, and compare with other methodologies. The effectiveness of the proposed method is tested on a large dataset that contains tweets of 180K user over 70 day period. The experimental results show that our proposed method identifies influencers successfully when used with a hybrid user specific feature that contains follower count and authenticity information, and is a highly scalable and extensible algorithm.},
	journal = {Knowledge-Based Systems},
	author = {Alp, Zeynep Zengin and Öğüdücü, Şule Gündüz},
	year = {2019},
	keywords = {Collaborative filtering, Data mining, Influence analysis, Influence maximization, Influence prediction, Matrix Factorization, Social media analysis},
	pages = {944 -- 954}
}

@article{gayo-avello_nepotistic_2013,
	title = {Nepotistic relationships in {Twitter} and their impact on rank prestige algorithms},
	volume = {49},
	issn = {0306-4573},
	url = {http://www.sciencedirect.com/science/article/pii/S030645731300071X},
	doi = {https://doi.org/10.1016/j.ipm.2013.06.003},
	abstract = {Micro-blogging services such as Twitter allow anyone to publish anything, anytime. Needless to say, many of the available contents can be diminished as babble or spam. However, given the number and diversity of users, some valuable pieces of information should arise from the stream of tweets. Thus, such services can develop into valuable sources of up-to-date information (the so-called real-time web) provided a way to find the most relevant/trustworthy/authoritative users is available. Hence, this makes a highly pertinent question for which graph centrality methods can provide an answer. In this paper the author offers a comprehensive survey of feasible algorithms for ranking users in social networks, he examines their vulnerabilities to linking malpractice in such networks, and suggests an objective criterion against which to compare such algorithms. Additionally, he suggests a first step towards “desensitizing” prestige algorithms against cheating by spammers and other abusive users.},
	number = {6},
	journal = {Information Processing \& Management},
	author = {Gayo-Avello, Daniel},
	year = {2013},
	keywords = {Graph centrality, Prestige, Social networks, Spamming, Twitter},
	pages = {1250 -- 1280}
}

@article{morone_influence_2015,
	title = {Influence maximization in complex networks through optimal percolation},
	volume = {524},
	url = {https://doi.org/10.1038/nature14604},
	journal = {Nature},
	author = {Morone, Flaviano and Makse, Hernán A.},
	month = jul,
	year = {2015},
	pages = {65}
}

@misc{noauthor_author-year_nodate,
	title = {Author-{Year} {Citation} with {Number} in {References}},
	url = {https://latex.org/forum/viewtopic.php?t=22505},
	urldate = {2019-01-10}
}

@article{gao_event_2017,
	title = {Event {Classification} in {Microblogs} via {Social} {Tracking}},
	volume = {8},
	issn = {2157-6904},
	url = {http://doi.acm.org/10.1145/2967502},
	doi = {10.1145/2967502},
	abstract = {Social media websites have become important information sharing platforms. The rapid development of social media platforms has led to increasingly large-scale social media data, which has shown remarkable societal and marketing values. There are needs to extract important events in live social media streams. However, microblogs event classification is challenging due to two facts, i.e., the short/conversational nature and the incompatible meanings between the text and the corresponding image in social posts, and the rapidly evolving contents. In this article, we propose to conduct event classification via deep learning and social tracking. First, we introduce a Multi-modal Multi-instance Deep Network (M2DN) for microblogs classification, which is able to handle the weakly labeled microblogs data oriented from the incompatible meanings inside microblogs. Besides predicting each microblogs as predefined events, we propose to employ social tracking to extract social-related auxiliary information to enrich the testing samples. We extract a set of candidate-relevant microblogs in a short time window by using social connections, such as related users and geographical locations. All these selected microblogs and the testing data are formulated in a Markov Random Field model. The inference on the Markov Random Field is conducted to update the classification results of the testing microblogs. This method is evaluated on the Brand-Social-Net dataset for classification of 20 events. Experimental results and comparison with the state of the arts show that the proposed method can achieve better performance for the event classification task.},
	number = {3},
	urldate = {2019-01-09},
	journal = {ACM Trans. Intell. Syst. Technol.},
	author = {Gao, Yue and Zhang, Hanwang and Zhao, Xibin and Yan, Shuicheng},
	month = feb,
	year = {2017},
	keywords = {Event classification, Markov Random Field (MRF), multi-instance, multi-modal, social tracking},
	pages = {35:1--35:14}
}

@article{samuel_location_2018,
	title = {Location estimation of non-geo-tagged tweets},
	issn = {1864-5917},
	url = {https://doi.org/10.1007/s12065-018-0163-3},
	doi = {10.1007/s12065-018-0163-3},
	abstract = {Internet users are getting more and more dependent for information regarding their daily lives. Most of the users are connected to each other using social networks. Social networking sites not only helps the users to connect and talk to each other but also share information with each other. Twitter [1] users attach their location information with the post or tweet to show their presence at the location. But, not all users tags or integrate the location information within the post. If a person wants to obtain the latest updates about an event then he/she have to go through all the tweets about that event, which is impossible because nearly 500 million tweets are posted on Twitter on a daily basis. Using Twitter the users can post up to 140 characters in their posts or tweet. Also, the tweets that originate from the location of the event are latest and contain new facts and the rest of the tweets convey that information only. Non-geo-tagged tweets are eliminated by the traditional systems. This paper presents a method to tag the non-geo-tagged tweets with the location then the user would be able to obtain the latest information by including the new. The proposed method performs better than previous methods and yields better results.},
	language = {en},
	urldate = {2019-01-09},
	journal = {Evolutionary Intelligence},
	author = {Samuel, Avinash and Sharma, Dilip Kumar},
	month = aug,
	year = {2018},
	keywords = {Geo-tagging, Information retrieval, Location estimation, Tweet, Twitter}
}

@article{abdelhaq_eventweet:_2013,
	title = {{EvenTweet}: {Online} {Localized} {Event} {Detection} from {Twitter}},
	volume = {6},
	issn = {2150-8097},
	shorttitle = {{EvenTweet}},
	url = {http://dx.doi.org/10.14778/2536274.2536307},
	doi = {10.14778/2536274.2536307},
	abstract = {Microblogging services such as Twitter, Facebook, and Foursquare have become major sources for information about real-world events. Most approaches that aim at extracting event information from such sources typically use the temporal context of messages. However, exploiting the location information of georeferenced messages, too, is important to detect localized events, such as public events or emergency situations. Users posting messages that are close to the location of an event serve as human sensors to describe an event. In this demonstration, we present a novel framework to detect localized events in real-time from a Twitter stream and to track the evolution of such events over time. For this, spatio-temporal characteristics of keywords are continuously extracted to identify meaningful candidates for event descriptions. Then, localized event information is extracted by clustering keywords according to their spatial similarity. To determine the most important events in a (recent) time frame, we introduce a scoring scheme for events. We demonstrate the functionality of our system, called Even-Tweet, using a stream of tweets from Europe during the 2012 UEFA European Football Championship.},
	number = {12},
	urldate = {2019-01-09},
	journal = {Proc. VLDB Endow.},
	author = {Abdelhaq, Hamed and Sengstock, Christian and Gertz, Michael},
	month = aug,
	year = {2013},
	keywords = {event detection, localized events, online approach},
	pages = {1326--1329}
}

@article{abdelhaq_eventweet:_2013-1,
	title = {{EvenTweet}: online localized event detection from twitter},
	volume = {6},
	issn = {21508097},
	shorttitle = {{EvenTweet}},
	url = {http://dl.acm.org/citation.cfm?doid=2536274.2536307},
	doi = {10.14778/2536274.2536307},
	abstract = {Microblogging services such as Twitter, Facebook, and Foursquare have become major sources for information about real-world events. Most approaches that aim at extracting event information from such sources typically use the temporal context of messages. However, exploiting the location information of georeferenced messages, too, is important to detect localized events, such as public events or emergency situations. Users posting messages that are close to the location of an event serve as human sensors to describe an event. In this demonstration, we present a novel framework to detect localized events in real-time from a Twitter stream and to track the evolution of such events over time. For this, spatio-temporal characteristics of keywords are continuously extracted to identify meaningful candidates for event descriptions. Then, localized event information is extracted by clustering keywords according to their spatial similarity. To determine the most important events in a (recent) time frame, we introduce a scoring scheme for events. We demonstrate the functionality of our system, called EvenTweet, using a stream of tweets from Europe during the 2012 UEFA European Football Championship.},
	language = {en},
	number = {12},
	urldate = {2019-01-09},
	journal = {Proceedings of the VLDB Endowment},
	author = {Abdelhaq, Hamed and Sengstock, Christian and Gertz, Michael},
	month = aug,
	year = {2013},
	pages = {1326--1329}
}

@inproceedings{utomo_geolocation_2018,
	title = {Geolocation prediction in social media data using text analysis: {A} review},
	shorttitle = {Geolocation prediction in social media data using text analysis},
	doi = {10.1109/ICOIACT.2018.8350674},
	abstract = {Geolocation information from social media data is essential for conducting geolocation-based analyzes such as traffic analysis and tourism analysis. However, geolocation information on social media data is still very limited. Only about 0.87\% to 3\% of data are geotagged data. Geolocation Prediction (GP) becomes a solution to overcome the problem. There are various approach to conduct Geolocation Prediction and each approach may give different result of location. The selection of the Geolocation Prediction approach then become important. Selected approach must be suitable for the needs of the analysis conducted. This paper focuses on reviewing geolocation prediction approaches based on text analysis in social media data. The review result shows that geolocation prediction approaches can be categorized into two categories called Content-based Geolocation Prediction and User-profiling-based Geolocation Prediction. This review further concludes that Content-based Geolocation Prediction is suitable for addressing geotagged data limitations in Location-specific Analysis because the location prediction results are specific to place-level. While combination approach is suitable to overcome the problem of geotagged data limitations on Location-distribution Analysis because it produces predictions of location at higher levels such as city-level, province-level, and country-level.},
	booktitle = {2018 {International} {Conference} on {Information} and {Communications} {Technology} ({ICOIACT})},
	author = {Utomo, M. N. Y. and Adji, T. B. and Ardiyanto, I.},
	month = mar,
	year = {2018},
	keywords = {Data mining, Geology, Google, Tagging, Twitter, Urban areas, content-based geolocation prediction, geocoding, geographic information systems, geolocation information, geolocation prediction, geolocation prediction approach, geolocation-based analysis, geotagged data, geotagged data limitations, location extraction, social media, social media data, social networking (online), text analysis},
	pages = {84--89}
}

@article{nguyen_real-time_2017,
	title = {Real-time event detection for online behavioral analysis of big social data},
	volume = {66},
	issn = {0167-739X},
	url = {http://www.sciencedirect.com/science/article/pii/S0167739X16300899},
	doi = {https://doi.org/10.1016/j.future.2016.04.012},
	abstract = {Social networking services are becoming increasingly popular during the daily lives of Internet citizens, especially since the advent of smart mobile devices with integrated utility modules such as 4G/WIFI connectivity, global positioning services, cameras, and heart beat sensors. Many devices are available for sharing information at any time, which can be listed by posting a photo, sharing a status, or narrating an event. The behavior of users means that the flow of data (or a social data stream) has real-time characteristics, which actually comprise notifications about your friends’ posts after a short delay for diffusion over the network. The data stream contains news pieces related to real social facts as well as unfocused information. In addition, important information (or events) attracts more public attention, which is demonstrated by the number of relevant messages or communication interactions between people interested in specific topics. From a technical perspective, the characteristics of data in the aforementioned scenario provide us with an opportunity to construct a model that can automatically determine the occurrence of events based on a social data stream. In this study, we propose an approach to solve the problem of early event identification, which requires appropriate approaches for processing incoming data in terms of the processing performance and number of data.},
	journal = {Future Generation Computer Systems},
	author = {Nguyen, Duc T. and Jung, Jai E.},
	year = {2017},
	keywords = {Real-time event detection, Social network analysis, ​Event detection},
	pages = {137 -- 145}
}

@article{nguyen_real-time_2017-1,
	title = {Real-time event detection for online behavioral analysis of big social data},
	volume = {66},
	issn = {0167-739X},
	url = {http://www.sciencedirect.com/science/article/pii/S0167739X16300899},
	doi = {https://doi.org/10.1016/j.future.2016.04.012},
	abstract = {Social networking services are becoming increasingly popular during the daily lives of Internet citizens, especially since the advent of smart mobile devices with integrated utility modules such as 4G/WIFI connectivity, global positioning services, cameras, and heart beat sensors. Many devices are available for sharing information at any time, which can be listed by posting a photo, sharing a status, or narrating an event. The behavior of users means that the flow of data (or a social data stream) has real-time characteristics, which actually comprise notifications about your friends’ posts after a short delay for diffusion over the network. The data stream contains news pieces related to real social facts as well as unfocused information. In addition, important information (or events) attracts more public attention, which is demonstrated by the number of relevant messages or communication interactions between people interested in specific topics. From a technical perspective, the characteristics of data in the aforementioned scenario provide us with an opportunity to construct a model that can automatically determine the occurrence of events based on a social data stream. In this study, we propose an approach to solve the problem of early event identification, which requires appropriate approaches for processing incoming data in terms of the processing performance and number of data.},
	journal = {Future Generation Computer Systems},
	author = {Nguyen, Duc T. and Jung, Jai E.},
	year = {2017},
	keywords = {Real-time event detection, Social network analysis, ​Event detection},
	pages = {137 -- 145}
}

@article{hossny_event_2019,
	title = {Event detection in {Twitter}: {A} keyword volume approach},
	shorttitle = {Event detection in {Twitter}},
	url = {http://arxiv.org/abs/1901.00570},
	abstract = {Event detection using social media streams needs a set of informative features with strong signals that need minimal preprocessing and are highly associated with events of interest. Identifying these informative features as keywords from Twitter is challenging, as people use informal language to express their thoughts and feelings. This informality includes acronyms, misspelled words, synonyms, transliteration and ambiguous terms. In this paper, we propose an efficient method to select the keywords frequently used in Twitter that are mostly associated with events of interest such as protests. The volume of these keywords is tracked in real time to identify the events of interest in a binary classification scheme. We use keywords within word-pairs to capture the context. The proposed method is to binarize vectors of daily counts for each word-pair by applying a spike detection temporal filter, then use the Jaccard metric to measure the similarity of the binary vector for each word-pair with the binary vector describing event occurrence. The top n word-pairs are used as features to classify any day to be an event or non-event day. The selected features are tested using multiple classifiers such as Naive Bayes, SVM, Logistic Regression, KNN and decision trees. They all produced AUC ROC scores up to 0.91 and F1 scores up to 0.79. The experiment is performed using the English language in multiple cities such as Melbourne, Sydney and Brisbane as well as the Indonesian language in Jakarta. The two experiments, comprising different languages and locations, yielded similar results.},
	urldate = {2019-01-08},
	journal = {arXiv:1901.00570 [cs]},
	author = {Hossny, Ahmad Hany and Mitchell, Lewis},
	month = jan,
	year = {2019},
	note = {arXiv: 1901.00570},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Social and Information Networks}
}

@article{hasan_survey_2018,
	title = {A survey on real-time event detection from the {Twitter} data stream},
	volume = {44},
	issn = {0165-5515},
	url = {https://doi.org/10.1177/0165551517698564},
	doi = {10.1177/0165551517698564},
	abstract = {The proliferation of social networking services has resulted in a rapid growth of their user base, spanning across the world. The collective information generated from these online platforms is overwhelming, in terms of both the amount of content produced every moment and the diversity of topics discussed. The real-time nature of the information produced by users has prompted researchers to analyse this content, in order to gain timely insight into the current state of affairs. Specifically, the microblogging service Twitter has been a recent focus of researchers to gather information on events occurring in real time. This article presents a survey of a wide variety of event detection methods applied to streaming Twitter data, classifying them according to shared common traits, and then discusses different aspects of the subtasks and challenges involved in event detection. We believe this survey will act as a guide and starting point for aspiring researchers to gain a structured view on state-of-the-art real-time event detection and spur further research in this direction.},
	language = {en},
	number = {4},
	urldate = {2019-01-08},
	journal = {Journal of Information Science},
	author = {Hasan, Mahmud and Orgun, Mehmet A and Schwitter, Rolf},
	month = aug,
	year = {2018},
	pages = {443--463}
}

@inproceedings{motamedi_characterizing_2018,
	title = {On {Characterizing} the {Twitter} {Elite} {Network}},
	doi = {10.1109/ASONAM.2018.8508252},
	abstract = {The most-followed Twitter users and their pairwise relationships form a sub-graph of all Twitter users that we call the Twitter elite network. The connectivity patterns and influence (in terms of reply and retweet activity) among these elite users illustrate how the “important” users connect and interact with one another on Twitter. Such an elite-focused view also provides valuable information about the structure of the Twitter network as a whole. This paper presents the first detailed characterization of the top-10K Twitter elite network. We describe a new technique to efficiently and accurately capture the Twitter elite network along with social attributes of individual elite accounts. We show that a sufficiently large elite network is typically composed of 15-20 resilient and socially cohesive communities representing “socially meaningful” components of the elite network. We then characterize the community-level structure of the elite network in terms of bias in directed pairwise connectivity and relative reachability. We demonstrate that both the retweet and reply activity between elite users are effectively contained within individual elite communities. Finally, we illustrate that a majority of the elite friends of regular Twitter users tend to belong to a single elite community. This finding offers a promising criterion to group regular users into “shadow partitions” based on their association with elite communities. We show that the level of overall inter-connectivity between shadow partitions mirrors the inter-connectivity of the elite communities. This suggests that these shadow partitions can be viewed as extensions of their corresponding elite communities.},
	booktitle = {2018 {IEEE}/{ACM} {International} {Conference} on {Advances} in {Social} {Networks} {Analysis} and {Mining} ({ASONAM})},
	author = {Motamedi, R. and Rezayi, S. and Rejaie, R. and Willinger, W.},
	month = aug,
	year = {2018},
	keywords = {Cultural differences, Data collection, Measurement, Mirrors, Periodic structures, Twitter, Twitter elite network characterization, Twitter network structure, community-level structure, connectivity patterns, directed pairwise connectivity, elite accounts, elite community, elite friends, elite users, elite-focused view, most-followed Twitter users, network theory (graphs), regular Twitter users, relative reachability, reply activity, resilient communities, retweet activity, shadow partitions, social attributes, social networking (online), socially cohesive communities},
	pages = {234--241}
}

@article{nguyen_real-time_2017-2,
	title = {Real-time event detection for online behavioral analysis of big social data},
	volume = {66},
	issn = {0167-739X},
	url = {http://www.sciencedirect.com/science/article/pii/S0167739X16300899},
	doi = {10.1016/j.future.2016.04.012},
	abstract = {Social networking services are becoming increasingly popular during the daily lives of Internet citizens, especially since the advent of smart mobile devices with integrated utility modules such as 4G/WIFI connectivity, global positioning services, cameras, and heart beat sensors. Many devices are available for sharing information at any time, which can be listed by posting a photo, sharing a status, or narrating an event. The behavior of users means that the flow of data (or a social data stream) has real-time characteristics, which actually comprise notifications about your friends’ posts after a short delay for diffusion over the network. The data stream contains news pieces related to real social facts as well as unfocused information. In addition, important information (or events) attracts more public attention, which is demonstrated by the number of relevant messages or communication interactions between people interested in specific topics. From a technical perspective, the characteristics of data in the aforementioned scenario provide us with an opportunity to construct a model that can automatically determine the occurrence of events based on a social data stream. In this study, we propose an approach to solve the problem of early event identification, which requires appropriate approaches for processing incoming data in terms of the processing performance and number of data.},
	urldate = {2019-01-07},
	journal = {Future Generation Computer Systems},
	author = {Nguyen, Duc T. and Jung, Jai E.},
	month = jan,
	year = {2017},
	keywords = {Real-time event detection, Social network analysis, ​Event detection},
	pages = {137--145}
}

@misc{noauthor_survey_nodate,
	title = {A survey on real-time event detection from the {Twitter} data stream - {Mahmud} {Hasan}, {Mehmet} {A} {Orgun}, {Rolf} {Schwitter}, 2018},
	url = {https://journals.sagepub.com/doi/abs/10.1177/0165551517698564},
	urldate = {2019-01-07}
}

@inproceedings{omar_influence_2017,
	title = {The influence of {Twitter} on education policy making},
	doi = {10.1109/SNPD.2017.8022712},
	abstract = {The explosive growth and influence of social media and microblogging sites has paved a way for internet users to freely express their views, opinions and share their experiences with others. With over 319 million users, Twitter presents a unique and dynamic source of information on people's interests and opinions and a rich source of data for sentiment analysis. This paper will discuss the influence of twitter on education policy making. The study aims at helping education policy makers to develop and implement policies that responds to the publics needs.},
	booktitle = {2017 18th {IEEE}/{ACIS} {International} {Conference} on {Software} {Engineering}, {Artificial} {Intelligence}, {Networking} and {Parallel}/{Distributed} {Computing} ({SNPD})},
	author = {Omar, M. S. and Njeru, A. and Yi, S.},
	month = jun,
	year = {2017},
	keywords = {Data mining, Data visualization, Education, Education Data Mining (EDM), Education Policy Making, Feature extraction, Influence of Twitter, Internet, Internet users, Naive Bayes, Sentiment Analysis, Sentiment analysis, Twitter, education policy makers, education policy making, educational computing, microblogging sites, sentiment analysis, social media, social networking (online)},
	pages = {133--136}
}
@article{himelboim_classifying_2017,
	title = {Classifying {Twitter} {Topic}-{Networks} {Using} {Social} {Network} {Analysis}},
	volume = {3},
	issn = {2056-3051},
	url = {https://doi.org/10.1177/2056305117691545},
	doi = {10.1177/2056305117691545},
	abstract = {As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the value of these network structures by segmenting 60 Twitter topical social media network datasets into these six distinct patterns of collective connections, illustrating how different topics of conversations exhibit different patterns of information flow. We discuss conceptual and practical implications for each structure.},
	language = {en},
	number = {1},
	urldate = {2019-01-07},
	journal = {Social Media + Society},
	author = {Himelboim, Itai and Smith, Marc A. and Rainie, Lee and Shneiderman, Ben and Espina, Camila},
	month = jan,
	year = {2017},
	pages = {2056305117691545}
}

@inproceedings{pal_identifying_2011,
	address = {New York, NY, USA},
	series = {{WSDM} '11},
	title = {Identifying {Topical} {Authorities} in {Microblogs}},
	isbn = {978-1-4503-0493-1},
	url = {http://doi.acm.org/10.1145/1935826.1935843},
	doi = {10.1145/1935826.1935843},
	abstract = {Content in microblogging systems such as Twitter is produced by tens to hundreds of millions of users. This diversity is a notable strength, but also presents the challenge of finding the most interesting and authoritative authors for any given topic. To address this, we first propose a set of features for characterizing social media authors, including both nodal and topical metrics. We then show how probabilistic clustering over this feature space, followed by a within-cluster ranking procedure, can yield a final list of top authors for a given topic. We present results across several topics, along with results from a user study confirming that our method finds authors who are significantly more interesting and authoritative than those resulting from several baseline conditions. Additionally our algorithm is computationally feasible in near real-time scenarios making it an attractive alternative for capturing the rapidly changing dynamics of microblogs.},
	urldate = {2019-01-07},
	booktitle = {Proceedings of the {Fourth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}},
	publisher = {ACM},
	author = {Pal, Aditya and Counts, Scott},
	year = {2011},
	keywords = {authority, clustering, microblogging, ranking, twitter},
	pages = {45--54}
}

@inproceedings{weng_twitterrank:_2010,
	address = {New York, NY, USA},
	series = {{WSDM} '10},
	title = {{TwitterRank}: {Finding} {Topic}-sensitive {Influential} {Twitterers}},
	isbn = {978-1-60558-889-6},
	shorttitle = {{TwitterRank}},
	url = {http://doi.acm.org/10.1145/1718487.1718520},
	doi = {10.1145/1718487.1718520},
	abstract = {This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4\% of the users in Twitter follow more than 80\% of their followers, and (2) 80.5\% of the users have 80\% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.},
	urldate = {2019-01-07},
	booktitle = {Proceedings of the {Third} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}},
	publisher = {ACM},
	author = {Weng, Jianshu and Lim, Ee-Peng and Jiang, Jing and He, Qi},
	year = {2010},
	keywords = {influential, pagerank, twitter},
	pages = {261--270}
}

@article{fan_octopus:_2018,
	title = {{OCTOPUS}: {An} online topic-aware influence analysis system for social networks},
	shorttitle = {{OCTOPUS}},
	url = {https://ink.library.smu.edu.sg/sis_research/4008},
	journal = {IEEE International Conference on Data Engineering},
	author = {FAN, Ju and QIU, Jiarong and LI, Yuchen and MENG, Qingfei and ZHANG, Dongxiang and LI, Guoliang and TAN, Kian-Lee and DU, Xiaoyong},
	month = apr,
	year = {2018}
}

@article{alp_influence_2019,
	title = {Influence {Factorization} for identifying authorities in {Twitter}},
	volume = {163},
	issn = {0950-7051},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705118305069},
	doi = {https://doi.org/10.1016/j.knosys.2018.10.020},
	abstract = {Prevalent usage of social media attracted companies and researchers to analyze its dynamics and effects on user behavior. One of the most intriguing aspects of social networks is to identify influencers who are experts on a specific topic. With the identification of these users within the network, many applications can be built for user recommendation, information diffusion modeling, viral marketing, user modeling and many more. In this paper, we aim to identify topic-based experts using a large dataset collected from Twitter. Our proposed approach has three phases: (1) identification of topics on social media posts (more specifically, tweets), (2) user modeling, based on a group of user specific features, and (3) Influence Factorization to identify topical influencers. The main advantage of the proposed method is to identify future influencers as well as current ones on Twitter. Moreover, it is an easy to implement algorithm using Spark MLlib, which can be easily extended to include other user specific features, and compare with other methodologies. The effectiveness of the proposed method is tested on a large dataset that contains tweets of 180K user over 70 day period. The experimental results show that our proposed method identifies influencers successfully when used with a hybrid user specific feature that contains follower count and authenticity information, and is a highly scalable and extensible algorithm.},
	journal = {Knowledge-Based Systems},
	author = {Alp, Zeynep Zengin and Öğüdücü, Şule Gündüz},
	year = {2019},
	keywords = {Collaborative filtering, Data mining, Influence analysis, Influence maximization, Influence prediction, Matrix Factorization, Social media analysis},
	pages = {944 -- 954}
}

@article{alp_influence_2019-1,
	title = {Influence {Factorization} for identifying authorities in {Twitter}},
	volume = {163},
	issn = {0950-7051},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705118305069},
	doi = {10.1016/j.knosys.2018.10.020},
	abstract = {Prevalent usage of social media attracted companies and researchers to analyze its dynamics and effects on user behavior. One of the most intriguing aspects of social networks is to identify influencers who are experts on a specific topic. With the identification of these users within the network, many applications can be built for user recommendation, information diffusion modeling, viral marketing, user modeling and many more. In this paper, we aim to identify topic-based experts using a large dataset collected from Twitter. Our proposed approach has three phases: (1) identification of topics on social media posts (more specifically, tweets), (2) user modeling, based on a group of user specific features, and (3) Influence Factorization to identify topical influencers. The main advantage of the proposed method is to identify future influencers as well as current ones on Twitter. Moreover, it is an easy to implement algorithm using Spark MLlib, which can be easily extended to include other user specific features, and compare with other methodologies. The effectiveness of the proposed method is tested on a large dataset that contains tweets of 180K user over 70 day period. The experimental results show that our proposed method identifies influencers successfully when used with a hybrid user specific feature that contains follower count and authenticity information, and is a highly scalable and extensible algorithm.},
	urldate = {2019-01-07},
	journal = {Knowledge-Based Systems},
	author = {Alp, Zeynep Zengin and Öğüdücü, Şule Gündüz},
	month = jan,
	year = {2019},
	keywords = {Collaborative filtering, Data mining, Influence analysis, Influence maximization, Influence prediction, Matrix Factorization, Social media analysis},
	pages = {944--954}
}

@article{riquelme_measuring_2016,
	title = {Measuring user influence on {Twitter}: {A} survey},
	volume = {52},
	issn = {0306-4573},
	shorttitle = {Measuring user influence on {Twitter}},
	url = {http://www.sciencedirect.com/science/article/pii/S0306457316300589},
	doi = {10.1016/j.ipm.2016.04.003},
	abstract = {Centrality is one of the most studied concepts in social network analysis. There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network. The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality. We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users. Twitter has different types of users, but the greatest utility lies in finding the most influential ones. The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature. These measures are very diverse. Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models. Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet. Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions. We consider all these aspects, and some additional ones. Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context.},
	number = {5},
	urldate = {2019-01-07},
	journal = {Information Processing \& Management},
	author = {Riquelme, Fabián and González-Cantergiani, Pablo},
	month = sep,
	year = {2016},
	keywords = {Centrality measure, Opinion leader, PageRank, Twitter, User influence},
	pages = {949--975}
}

@inproceedings{cha_measuring_2010,
	title = {Measuring {User} {Influence} in {Twitter}: {The} {Million} {Follower} {Fallacy}},
	shorttitle = {Measuring {User} {Influence} in {Twitter}},
	abstract = {Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user’s influence on others—a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.},
	booktitle = {{ICWSM}},
	author = {Cha, Meeyoung and Haddadi, Hamed and Benevenuto, Fabrício and Gummadi, Krishna P.},
	year = {2010},
	keywords = {Directed graph, Hyperlink, Social media}
}

@inproceedings{backstrom_find_2010,
	title = {Find me if you can: improving geographical prediction with social and spatial proximity},
	shorttitle = {Find me if you can},
	doi = {10.1145/1772690.1772698},
	abstract = {Geography and social relationships are inextricably intertwined; the people we interact with on a daily basis almost always live near us. As people spend more time online, data regarding these two dimensions -- geography and social relationships -- are becoming increasingly precise, allowing us to build reliable models to describe their interaction. These models have important implications in the design of location-based services, security intrusion detection, and social media supporting local communities.
 Using user-supplied address data and the network of associations between members of the Facebook social network, we can directly observe and measure the relationship between geography and friendship. Using these measurements, we introduce an algorithm that predicts the location of an individual from a sparse set of located users with performance that exceeds IP-based geolocation. This algorithm is efficient and scalable, and could be run on a network containing hundreds of millions of users.},
	booktitle = {{WWW}},
	author = {Backstrom, Lars and Sun, Eric and Marlow, Cameron},
	year = {2010},
	keywords = {Algorithm, Community, Dental Intrusion, Geography, Geolocation, Intrusion detection system, Location-based service, Mental association, Providing (action), Scalability, Social media, Social network, Sparse language, Sparse matrix}
}

@inproceedings{han_geolocation_2012,
	address = {Mumbai, India},
	title = {Geolocation {Prediction} in {Social} {Media} {Data} by {Finding} {Location} {Indicative} {Words}},
	url = {http://aclweb.org/anthology/C12-1064},
	urldate = {2019-01-07},
	booktitle = {Proceedings of {COLING} 2012},
	publisher = {The COLING 2012 Organizing Committee},
	author = {Han, Bo and Cook, Paul and Baldwin, Timothy},
	year = {2012},
	pages = {1045--1062}
}

@article{mahmud_home_2014,
	title = {Home {Location} {Identification} of {Twitter} {Users}},
	url = {http://arxiv.org/abs/1403.2345},
	abstract = {We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone or geographic region, using the content of users tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations and makes use of a geographic gazetteer dictionary to identify place-name entities. We find that a hierarchical classification approach, where time zone, state or geographic region is predicted first and city is predicted next, can improve prediction accuracy. We have also analyzed movement variations of Twitter users, built a classifier to predict whether a user was travelling in a certain period of time and use that to further improve the location detection accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the home location of Twitter users.},
	urldate = {2019-01-07},
	journal = {arXiv:1403.2345 [cs]},
	author = {Mahmud, Jalal and Nichols, Jeffrey and Drews, Clemens},
	month = mar,
	year = {2014},
	note = {arXiv: 1403.2345},
	keywords = {Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Social and Information Networks}
}

@inproceedings{cheng_you_2010,
	title = {You are where you tweet: a content-based approach to geo-locating twitter users},
	shorttitle = {You are where you tweet},
	doi = {10.1145/1871437.1871535},
	abstract = {We propose and evaluate a probabilistic framework for estimating a Twitter user's city-level location based purely on the content of the user's tweets, even in the absence of any other geospatial cues. By augmenting the massive human-powered sensing capabilities of Twitter and related microblogging services with content-derived location information, this framework can overcome the sparsity of geo-enabled features in these services and enable new location-based personalized information services, the targeting of regional advertisements, and so on. Three of the key features of the proposed approach are: (i) its reliance purely on tweet content, meaning no need for user IP information, private login information, or external knowledge bases; (ii) a classification component for automatically identifying words in tweets with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate. The system estimates k possible locations for each user in descending order of confidence. On average we find that the location estimates converge quickly (needing just 100s of tweets), placing 51\% of Twitter users within 100 miles of their actual location.},
	booktitle = {{CIKM}},
	author = {Cheng, Zhiyuan and Caverlee, James and Lee, Kyumin},
	year = {2010},
	keywords = {Converge, Login, Personalization, Smoothing, Sorting, Sparse matrix}
}

@article{becker_beyond_nodate,
	title = {Beyond {Trending} {Topics}: {Real}-{World} {Event} {Identiﬁcation} on {Twitter}},
	abstract = {User-contributed messages on social media sites such as Twitter have emerged as powerful, real-time means of information sharing on the Web. These short messages tend to reﬂect a variety of events in real time, earlier than other social media sites such as Flickr or YouTube, making Twitter particularly well suited as a source of real-time event content. In this paper, we explore approaches for analyzing the stream of Twitter messages to distinguish between messages about real-world events and non-event messages. Our approach relies on a rich family of aggregate statistics of topically similar message clusters, including temporal, social, topical, and Twitter-centric features. Our large-scale experiments over millions of Twitter messages show the eﬀectiveness of our approach for surfacing real-world event content on Twitter.},
	language = {en},
	author = {Becker, Hila and Naaman, Mor and Gravano, Luis},
	pages = {17}
}

@misc{jun_wang_towards_nodate,
	type = {Technology},
	title = {Towards a {Social} {Media} {Analytics} {Platform}-- {Event} {Detection} and {User} …},
	url = {https://www.slideshare.net/hustwj/towards-a-social-media-analytics-platform-event-detection-and-user-profiling-for-twitter},
	abstract = {A Tutorial at WWW 2014},
	urldate = {2019-01-05},
	author = {Jun Wang}
}

@inproceedings{gupta_towards_2014,
	address = {New York, NY, USA},
	series = {{WWW} '14 {Companion}},
	title = {Towards a {Social} {Media} {Analytics} {Platform}: {Event} {Detection} and {User} {Profiling} for {Twitter}},
	isbn = {978-1-4503-2745-9},
	shorttitle = {Towards a {Social} {Media} {Analytics} {Platform}},
	url = {http://doi.acm.org/10.1145/2567948.2577264},
	doi = {10.1145/2567948.2577264},
	abstract = {Microblog data differs significantly from the traditional text data with respect to a variety of dimensions. Microblog data contains short documents, SMS kind of language, and is full of code mixing. Though a lot of it is mere social babble, it also contains fresh news coming from human sensors at a humungous rate. Given such interesting characteristics, the world wide web community has witnessed a large number of research tasks for microblogging platforms recently. Event detection on Twitter is one of the most popular such tasks with a large number of applications. The proposed tutorial on social analytics for Twitter will contain three parts. In the first part, we will discuss research efforts towards detection of events from Twitter using both the tweet content as well as other external sources. We will also discuss various applications for which event detection mechanisms have been put to use. Merely detecting events is not enough. Applications require that the detector must be able to provide a good description of the event as well. In the second part, we will focus on describing events using the best phrase, event type, event timespan, and credibility. In the third part, we will discuss user profiling for Twitter with a special focus on user location prediction. We will conclude with a summary and thoughts on future directions.},
	urldate = {2019-01-05},
	booktitle = {Proceedings of the 23rd {International} {Conference} on {World} {Wide} {Web}},
	publisher = {ACM},
	author = {Gupta, Manish and Li, Rui and Chang, Kevin Chen-Chuan},
	year = {2014},
	keywords = {event credibility, event detection, event timespan computation, event type prediction, location prediction, microblogs, social media analytics platform, twitter, www tutorial},
	pages = {193--194}
}

@article{kaleel_cluster-discovery_2015,
	title = {Cluster-discovery of {Twitter} messages for event detection and trending},
	volume = {6},
	issn = {1877-7503},
	url = {http://www.sciencedirect.com/science/article/pii/S1877750314001604},
	doi = {10.1016/j.jocs.2014.11.004},
	abstract = {Social media data carries abundant hidden occurrences of real-time events. In this paper, a novel methodology is proposed for detecting and trending events from tweet clusters that are discovered by using locality sensitive hashing (LSH) technique. Key challenges include: (1) construction of dictionary using incremental term frequency–inverse document frequency (TF–IDF) in high-dimensional data to create tweet feature vector, (2) leveraging LSH to find truly interesting events, (3) trending the behavior of event based on time, geo-locations and cluster size, and (4) speed-up the cluster-discovery process while retaining the cluster quality. Experiments are conducted for a specific event and the clusters discovered using LSH and K-means are compared with group average agglomerative clustering technique.},
	urldate = {2019-01-04},
	journal = {Journal of Computational Science},
	author = {Kaleel, Shakira Banu and Abhari, Abdolreza},
	month = jan,
	year = {2015},
	keywords = {Cluster-discovery, Data mining, Event detection, Event trending, Locality sensitive hashing, Term frequency–inverse document frequency, Web social networking},
	pages = {47--57}
}

@inproceedings{feng_streamcube:_2015,
	title = {{STREAMCUBE}: {Hierarchical} spatio-temporal hashtag clustering for event exploration over the {Twitter} stream},
	shorttitle = {{STREAMCUBE}},
	doi = {10.1109/ICDE.2015.7113425},
	abstract = {What is happening around the world? When and where? Mining the geo-tagged Twitter stream makes it possible to answer the above questions in real-time. Although a single tweet can be short and noisy, proper aggregations of tweets can provide meaningful results. In this paper, we focus on hierarchical spatio-temporal hashtag clustering techniques. Our system has the following features: (1) Exploring events (hashtag clusters) with different space granularity. Users can zoom in and out on maps to find out what is happening in a particular area. (2) Exploring events with different time granularity. Users can choose to see what is happening today or in the past week. (3) Efficient single-pass algorithm for event identification, which provides human-readable hashtag clusters. (4) Efficient event ranking which aims to find burst events and localized events given a particular region and time frame. To support aggregation with different space and time granularity, we propose a data structure called STREAMCUBE, which is an extension of the data cube structure from the database community with spatial and temporal hierarchy. To achieve high scalability, we propose a divide-and-conquer method to construct the STREAMCUBE. To support flexible event ranking with different weights, we proposed a top-k based index. Different efficient methods are used to speed up event similarity computations. Finally, we have conducted extensive experiments on a real twitter data. Experimental results show that our framework can provide meaningful results with high scalability.},
	booktitle = {2015 {IEEE} 31st {International} {Conference} on {Data} {Engineering}},
	author = {Feng, W. and Zhang, C. and Zhang, W. and Han, J. and Wang, J. and Aggarwal, C. and Huang, J.},
	month = apr,
	year = {2015},
	keywords = {Clustering algorithms, Media, Noise measurement, Nominations and elections, Real-time systems, STREAMCUBE, Semantics, Twitter, Twitter stream, burst events, data cube structure, data mining, database community, database management systems, divide and conquer methods, divide-and-conquer method, event exploration, event identification, event ranking, event similarity computation, geo-tagged Twitter stream mining, geography, hierarchical spatio-temporal hashtag clustering, human-readable hashtag clusters, localized events, pattern clustering, single-pass algorithm, social networking (online), space granularity, spatial hierarchy, temporal hierarchy, time granularity, top-k based index},
	pages = {1561--1572}
}

@incollection{kursuncu_predictive_2019,
	address = {Cham},
	series = {Lecture {Notes} in {Social} {Networks}},
	title = {Predictive {Analysis} on {Twitter}: {Techniques} and {Applications}},
	isbn = {978-3-319-94105-9},
	shorttitle = {Predictive {Analysis} on {Twitter}},
	url = {https://doi.org/10.1007/978-3-319-94105-9_4},
	abstract = {Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches, and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories.},
	language = {en},
	urldate = {2019-01-04},
	booktitle = {Emerging {Research} {Challenges} and {Opportunities} in {Computational} {Social} {Network} {Analysis} and {Mining}},
	publisher = {Springer International Publishing},
	author = {Kursuncu, Ugur and Gaur, Manas and Lokala, Usha and Thirunarayan, Krishnaprasad and Sheth, Amit and Arpinar, I. Budak},
	editor = {Agarwal, Nitin and Dokoohaki, Nima and Tokdemir, Serpil},
	year = {2019},
	doi = {10.1007/978-3-319-94105-9_4},
	pages = {67--104}
}

@inproceedings{wang_unsupervised_2017,
	address = {New York, NY, USA},
	series = {{SocialSens}'17},
	title = {Unsupervised {Event} {Tracking} by {Integrating} {Twitter} and {Instagram}},
	isbn = {978-1-4503-4977-2},
	url = {http://doi.acm.org/10.1145/3055601.3055615},
	doi = {10.1145/3055601.3055615},
	abstract = {This paper proposes an unsupervised framework for tracking real world events from their traces on Twitter and Instagram. Empirical data suggests that event detection from Instagram streams errs on the false-negative side due to the relative sparsity of Instagram data (compared to Twitter data), whereas event detection from Twitter can suffer from false-positives, at least if not paired with careful analysis of tweet content. To tackle both problems simultaneously, we design a unified unsupervised algorithm that fuses events detected originally on Instagram (called I-events) and events detected originally on Twitter (called T-events), that occur in adjacent periods, in an attempt to combine the benefits of both sources while eliminating their individual disadvantages. We evaluate the proposed framework with real data crawled from Twitter and Instagram. The results indicate that our algorithm significantly improves tracking accuracy compared to baselines.},
	urldate = {2019-01-04},
	booktitle = {Proceedings of the 2Nd {International} {Workshop} on {Social} {Sensing}},
	publisher = {ACM},
	author = {Wang, Shiguang and Giridhar, Prasanna and Kaplan, Lance and Abdelzaher, Tarek},
	year = {2017},
	pages = {81--86}
}

@article{sato_credibility_2019,
	title = {Credibility {Evaluation} of {Twitter}-{Based} {Event} {Detection} by a {Mixing} {Analysis} of {Heterogeneous} {Data}},
	volume = {7},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2018.2886312},
	abstract = {Twitter has been recognized as an important data resource for real-time event detection. However, Twitter-based event detection systems cannot guarantee credibility in terms of their detection results. Rumor detection has been studied recently to enable credible event detection. Nevertheless, this problem has not yet been solved because most of the existing studies only focus on the information on Twitter with Twitter-based event detection systems. More specifically, the existing studies detect rumors by identifying and checking special features of incredible information on Twitter. However, values of the identified features can be faked easily and so it is important to conduct a mixing analysis of both Twitter and external credible data resources to solve this problem. The problem is how to harmoniously analyze heterogeneous data since they have different data formats, generation times, and so on. To solve this problem, this paper proposes a method to evaluate the credibility of Twitter-based event detection, which considers the two kinds of data resources for credibility evaluation to exclude influence by falsification. In particular, our method utilizes an event detection result and the number of articles related to the event. We performed comprehensive experiments to evaluate the proposed method. The experiments show that the proposed method gives the detected events high credibility and other events low credibility, correctly. More specifically, event detection accuracy increases by an average of 26.8\% by reviewing the detection results according to their credibility evaluated with the proposed method. Additionally, to measure the appropriateness of the credibility evaluation, we filtered out incorrectly detected events from the event detection results referencing their evaluated credibility based on the proposed method. We calculated the F-measure, precision, and recall of the experimental results, and through the experiments, we present the effectiveness of the method.},
	journal = {IEEE Access},
	author = {Sato, K. and Wang, J. and Cheng, Z.},
	year = {2019},
	keywords = {Computer science, Delays, Event detection, Feature extraction, Open area test sites, Proposals, TF-IDF, Twitter, big data analysis, credibility of Twitter-based event detection, extended hybrid TF-IDF, real-time event detection, rumor detection},
	pages = {1095--1106}
}

@article{guille_event_2015,
	title = {Event detection, tracking, and visualization in {Twitter}: a mention-anomaly-based approach},
	volume = {5},
	issn = {1869-5469},
	shorttitle = {Event detection, tracking, and visualization in {Twitter}},
	url = {https://doi.org/10.1007/s13278-015-0258-0},
	doi = {10.1007/s13278-015-0258-0},
	abstract = {The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper, we propose mention-anomaly-based event detection (MABED), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e., mentions) that users insert in tweets to detect significant events and estimate the magnitude of their impact over the crowd. MABED also differs from the literature in that it dynamically estimates the period of time during which each event is discussed, rather than assuming a predefined fixed duration for all events. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Qualitatively speaking, we find that MABED helps with the interpretation of detected events by providing clear textual descriptions and precise temporal descriptions. We also show how MABED can help understanding users’ interest. Furthermore, we describe three visualizations designed to favor an efficient exploration of the detected events.},
	language = {en},
	number = {1},
	urldate = {2019-01-04},
	journal = {Social Network Analysis and Mining},
	author = {Guille, Adrien and Favre, Cécile},
	month = may,
	year = {2015},
	keywords = {Event detection, Twitter, Visualization},
	pages = {18}
}

@inproceedings{xing_hashtag-based_2016,
	title = {Hashtag-{Based} {Sub}-{Event} {Discovery} {Using} {Mutually} {Generative} {LDA} in {Twitter}},
	booktitle = {{AAAI}},
	author = {Xing, Chen and Wang, Yuan and Liu, Jie and Huang, Yalou and Ma, Wei-Ying},
	year = {2016},
	keywords = {Hashtag}
}

@article{atefeh_survey_2015,
	title = {A {Survey} of {Techniques} for {Event} {Detection} in {Twitter}},
	volume = {31},
	copyright = {© 2013 Wiley Periodicals, Inc.},
	issn = {1467-8640},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/coin.12017},
	doi = {10.1111/coin.12017},
	abstract = {Twitter is among the fastest-growing microblogging and online social networking services. Messages posted on Twitter (tweets) have been reporting everything from daily life stories to the latest local and global news and events. Monitoring and analyzing this rich and continuous user-generated content can yield unprecedentedly valuable information, enabling users and organizations to acquire actionable knowledge. This article provides a survey of techniques for event detection from Twitter streams. These techniques aim at finding real-world occurrences that unfold over space and time. In contrast to conventional media, event detection from Twitter streams poses new challenges. Twitter streams contain large amounts of meaningless messages and polluted content, which negatively affect the detection performance. In addition, traditional text mining techniques are not suitable, because of the short length of tweets, the large number of spelling and grammatical errors, and the frequent use of informal and mixed language. Event detection techniques presented in literature address these issues by adapting techniques from various fields to the uniqueness of Twitter. This article classifies these techniques according to the event type, detection task, and detection method and discusses commonly used features. Finally, it highlights the need for public benchmarks to evaluate the performance of different detection approaches and various features.},
	language = {en},
	number = {1},
	urldate = {2019-01-04},
	journal = {Computational Intelligence},
	author = {Atefeh, Farzindar and Khreich, Wael},
	year = {2015},
	keywords = {Twitter data stream, event detection, event identification, microblogs, monitoring social media},
	pages = {132--164}
}

@inproceedings{li_tedas:_2012,
	title = {{TEDAS}: {A} {Twitter}-based {Event} {Detection} and {Analysis} {System}},
	shorttitle = {{TEDAS}},
	doi = {10.1109/ICDE.2012.125},
	abstract = {Witnessing the emergence of Twitter, we propose a Twitter-based Event Detection and Analysis System (TEDAS), which helps to (1) detect new events, to (2) analyze the spatial and temporal pattern of an event, and to (3) identify importance of events. In this demonstration, we show the overall system architecture, explain in detail the implementation of the components that crawl, classify, and rank tweets and extract location from tweets, and present some interesting results of our system.},
	booktitle = {2012 {IEEE} 28th {International} {Conference} on {Data} {Engineering}},
	author = {Li, R. and Lei, K. H. and Khadiwala, R. and Chang, K. C.},
	month = apr,
	year = {2012},
	keywords = {Conferences, Data engineering, TEDAS, Twitter-based event detection and analysis system, event spatial pattern, event temporal pattern, overall system architecture, social networking (online)},
	pages = {1273--1276}
}

@article{bruns_towards_2013,
	title = {Towards more systematic {Twitter} analysis: metrics for tweeting activities},
	volume = {16},
	issn = {1364-5579},
	shorttitle = {Towards more systematic {Twitter} analysis},
	url = {https://doi.org/10.1080/13645579.2012.756095},
	doi = {10.1080/13645579.2012.756095},
	abstract = {Twitter is an important and influential social media platform, but much research into its uses remains centred around isolated cases – e.g. of events in political communication, crisis communication, or popular culture, often coordinated by shared hashtags (brief keywords, prefixed with the symbol ‘\#’). In particular, a lack of standard metrics for comparing communicative patterns across cases prevents researchers from developing a more comprehensive perspective on the diverse, sometimes crucial roles which hashtags play in Twitter-based communication. We address this problem by outlining a catalogue of widely applicable, standardised metrics for analysing Twitter-based communication, with particular focus on hashtagged exchanges. We also point to potential uses for such metrics, presenting an indication of what broader comparisons of diverse cases can achieve.},
	number = {2},
	urldate = {2019-01-04},
	journal = {International Journal of Social Research Methodology},
	author = {Bruns, Axel and Stieglitz, Stefan},
	month = mar,
	year = {2013},
	keywords = {Erratum, Twitter, communicative exchanges, hashtags, metrics, social media},
	pages = {91--108}
}

@article{zuo_word_2014,
	title = {Word {Network} {Topic} {Model}: {A} {Simple} but {General} {Solution} for {Short} and {Imbalanced} {Texts}},
	shorttitle = {Word {Network} {Topic} {Model}},
	url = {http://arxiv.org/abs/1412.5404},
	abstract = {The short text has been the prevalent format for information of Internet in recent decades, especially with the development of online social media, whose millions of users generate a vast number of short messages everyday. Although sophisticated signals delivered by the short text make it a promising source for topic modeling, its extreme sparsity and imbalance brings unprecedented challenges to conventional topic models like LDA and its variants. Aiming at presenting a simple but general solution for topic modeling in short texts, we present a word co-occurrence network based model named WNTM to tackle the sparsity and imbalance simultaneously. Different from previous approaches, WNTM models the distribution over topics for each word instead of learning topics for each document, which successfully enhance the semantic density of data space without importing too much time or space complexity. Meanwhile, the rich contextual information preserved in the word-word space also guarantees its sensitivity in identifying rare topics with convincing quality. Furthermore, employing the same Gibbs sampling with LDA makes WNTM easily to be extended to various application scenarios. Extensive validations on both short and normal texts testify the outperformance of WNTM as compared to baseline methods. And finally we also demonstrate its potential in precisely discovering newly emerging topics or unexpected events in Weibo at pretty early stages.},
	urldate = {2019-01-04},
	journal = {arXiv:1412.5404 [cs]},
	author = {Zuo, Yuan and Zhao, Jichang and Xu, Ke},
	month = dec,
	year = {2014},
	note = {arXiv: 1412.5404},
	keywords = {Computer Science - Computation and Language, Computer Science - Information Retrieval}
}

@inproceedings{xun_topic_2016,
	title = {Topic {Discovery} for {Short} {Texts} {Using} {Word} {Embeddings}},
	doi = {10.1109/ICDM.2016.0176},
	abstract = {Discovering topics in short texts, such as news titles and tweets, has become an important task for many content analysis applications. However, due to the lack of rich context information in short texts, the performance of conventional topic models on short texts is usually unsatisfying. In this paper, we propose a novel topic model for short text corpus using word embeddings. Continuous space word embeddings, which is proven effective at capturing regularities in language, is incorporated into our model to provide additional semantics. Thus we model each short document as a Gaussian topic over word embeddings in the vector space. In addition, considering that background words in a short text are usually not semantically related, we introduce a discrete background mode over word types to complement the continuous Gaussian topics. We evaluate our model on news titles from data sources like abcnews, showing that our model is able to extract more coherent topics from short texts compared with the baseline methods and learn better topic representation for each short document.},
	booktitle = {2016 {IEEE} 16th {International} {Conference} on {Data} {Mining} ({ICDM})},
	author = {Xun, G. and Gopalakrishnan, V. and Ma, F. and Li, Y. and Gao, J. and Zhang, A.},
	month = dec,
	year = {2016},
	keywords = {Context, Electronic publishing, Encyclopedias, Gaussian distribution, Gaussian processes, Gaussian topic, Internet, Semantics, abcnews, baseline methods, content analysis applications, continuous space word embeddings, data sources, discrete background mode, news titles, short text corpus, short texts, text analysis, topic discovery, topic model, tweets, vector space, vectors, word embeddings},
	pages = {1299--1304}
}

@inproceedings{sridhar_unsupervised_2015,
	title = {Unsupervised {Topic} {Modeling} for {Short} {Texts} {Using} {Distributed} {Representations} of {Words}},
	abstract = {We present an unsupervised topic model for short texts that performs soft clustering over distributed representations of words. We model the low-dimensional semantic vector space represented by the dense distributed representations of words using Gaussian mixture models (GMMs) whose components capture the notion of latent topics. While conventional topic modeling schemes such as probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA) need aggregation of short messages to avoid data sparsity in short documents, our framework works on large amounts of raw short texts (billions of words). In contrast with other topic modeling frameworks that use word cooccurrence statistics, our framework uses a vector space model that overcomes the issue of sparse word co-occurrence patterns. We demonstrate that our framework outperforms LDA on short texts through both subjective and objective evaluation. We also show the utility of our framework in learning topics and classifying short texts on Twitter data for English, Spanish, French, Portuguese and Russian.},
	booktitle = {{VS}@{HLT}-{NAACL}},
	author = {Sridhar, Vivek Kumar Rangarajan},
	year = {2015},
	keywords = {Cluster analysis, Latent Dirichlet allocation, Mixture model, Probabilistic latent semantic analysis, Sparse matrix, Topic model, Unsupervised learning}
}

@misc{noauthor_python_nodate,
	title = {Python {JSON}},
	url = {https://www.w3schools.com/python/python_json.asp},
	urldate = {2018-11-02}
}

@misc{noauthor_crf_nodate,
	title = {{CRF} {Layer} on the {Top} of {BiLSTM} - 1},
	url = {http://createmomo.github.io/2017/09/12/CRF_Layer_on_the_Top_of_BiLSTM_1/index.html},
	abstract = {OutlineThe article series will include:  Introduction - the general idea of the CRF layer on the top of BiLSTM for named entity recognition tasks A Detailed Example -  a toy example to explain how CRF},
	urldate = {2018-10-17},
	journal = {CreateMoMo}
}

@misc{noauthor_computer_nodate,
	title = {Computer {Science} {PhD} {Qualifier} {Exam}},
	url = {http://people.cs.vt.edu/~bhuang/courses/quals17-18/},
	urldate = {2018-10-17}
}

@misc{noauthor_computer_nodate-1,
	title = {Computer {Science} {PhD} {Qualifier} {Exam}},
	url = {http://people.cs.vt.edu/~bhuang/courses/quals16-17/},
	urldate = {2018-10-17}
}

@article{akidau_dataflow_2015,
	title = {The dataflow model: a practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing},
	volume = {8},
	issn = {21508097},
	shorttitle = {The dataflow model},
	url = {http://dl.acm.org/citation.cfm?doid=2824032.2824076},
	doi = {10.14778/2824032.2824076},
	abstract = {Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves, in addition to an insatiable hunger for faster answers. Meanwhile, practicality dictates that one can never fully optimize along all dimensions of correctness, latency, and cost for these types of input. As a result, data processing practitioners are left with the quandary of how to reconcile the tensions between these seemingly competing propositions, often resulting in disparate implementations and systems.},
	language = {en},
	number = {12},
	urldate = {2018-10-08},
	journal = {Proceedings of the VLDB Endowment},
	author = {Akidau, Tyler and Schmidt, Eric and Whittle, Sam and Bradshaw, Robert and Chambers, Craig and Chernyak, Slava and Fernández-Moctezuma, Rafael J. and Lax, Reuven and McVeety, Sam and Mills, Daniel and Perry, Frances},
	month = aug,
	year = {2015},
	pages = {1792--1803}
}

@article{yang_husky:_2016,
	title = {Husky: towards a more efficient and expressive distributed computing framework},
	volume = {9},
	issn = {21508097},
	shorttitle = {Husky},
	url = {http://dl.acm.org/citation.cfm?doid=2876473.2876477},
	doi = {10.14778/2876473.2876477},
	abstract = {Finding efﬁcient, expressive and yet intuitive programming models for data-parallel computing system is an important and open problem. Systems like Hadoop and Spark have been widely adopted for massive data processing, as coarse-grained primitives like map and reduce are succinct and easy to master. However, sometimes over-simpliﬁed API hinders programmers from more ﬁne-grained control and designing more efﬁcient algorithms. Developers may have to resort to sophisticated domain-speciﬁc languages (DSLs), or even low-level layers like MPI, but this raises development cost—learning many mutually exclusive systems prolongs the development schedule, and the use of low-level tools may result in bugprone programming.},
	language = {en},
	number = {5},
	urldate = {2018-10-08},
	journal = {Proceedings of the VLDB Endowment},
	author = {Yang, Fan and Li, Jinfeng and Cheng, James},
	month = jan,
	year = {2016},
	pages = {420--431}
}

@article{li_multiple_2012,
	title = {Multiple location profiling for users and relationships from social network and content},
	volume = {5},
	issn = {21508097},
	url = {http://dl.acm.org/citation.cfm?doid=2350229.2350273},
	doi = {10.14778/2350229.2350273},
	abstract = {Users’ locations are important for many applications such as personalized search and localized content delivery. In this paper, we study the problem of proﬁling Twitter users’ locations with their following network and tweets. We propose a multiple location proﬁling model (MLP), which has three key features: 1) it formally models how likely a user follows another user given their locations and how likely a user tweets a venue given his location, 2) it fundamentally captures that a user has multiple locations and his following relationships and tweeted venues can be related to any of his locations, and some of them are even noisy, and 3) it novelly utilizes the home locations of some users as partial supervision. As a result, MLP not only discovers users’ locations accurately and completely, but also “explains” each following relationship by revealing users’ true locations in the relationship. Experiments on a large-scale data set demonstrate those advantages. Particularly, 1) for predicting users’ home locations, MLP successfully places 62\% users and outperforms two state-of-the-art methods by 10\% in accuracy, 2) for discovering users’ multiple locations, MLP improves the baseline methods by 14\% in recall, and 3) for explaining following relationships, MLP achieves 57\% accuracy.},
	language = {en},
	number = {11},
	urldate = {2018-10-08},
	journal = {Proceedings of the VLDB Endowment},
	author = {Li, Rui and Wang, Shengjie and Chang, Kevin Chen-Chuan},
	month = jul,
	year = {2012},
	pages = {1603--1614}
}

@inproceedings{li_towards_2012,
	address = {Beijing, China},
	title = {Towards social user profiling: unified and discriminative influence model for inferring home locations},
	isbn = {978-1-4503-1462-6},
	shorttitle = {Towards social user profiling},
	url = {http://dl.acm.org/citation.cfm?doid=2339530.2339692},
	doi = {10.1145/2339530.2339692},
	abstract = {Users’ locations are important to many applications such as targeted advertisement and news recommendation. In this paper, we focus on the problem of proﬁling users’ home locations in the context of social network (Twitter). The problem is nontrivial, because signals, which may help to identify a user’s location, are scarce and noisy. We propose a uniﬁed discriminative inﬂuence model, named as UDI, to solve the problem. To overcome the challenge of scarce signals, UDI integrates signals observed from both social network (friends) and user-centric data (tweets) in a uniﬁed probabilistic framework. To overcome the challenge of noisy signals, UDI captures how likely a user connects to a signal with respect to 1) the distance between the user and the signal, and 2) the inﬂuence scope of the signal. Based on the model, we develop local and global location prediction methods. The experiments on a large scale data set show that our methods improve the state-of-the-art methods by 13\%, and achieve the best performance.},
	language = {en},
	urldate = {2018-10-08},
	booktitle = {Proceedings of the 18th {ACM} {SIGKDD} international conference on {Knowledge} discovery and data mining - {KDD} '12},
	publisher = {ACM Press},
	author = {Li, Rui and Wang, Shengjie and Deng, Hongbo and Wang, Rui and Chang, Kevin Chen-Chuan},
	year = {2012},
	pages = {1023}
}

@inproceedings{li_tedas:_2012-1,
	title = {{TEDAS}: {A} {Twitter}-based {Event} {Detection} and {Analysis} {System}},
	shorttitle = {{TEDAS}},
	doi = {10.1109/ICDE.2012.125},
	abstract = {Witnessing the emergence of Twitter, we propose a Twitter-based Event Detection and Analysis System (TEDAS), which helps to (1) detect new events, to (2) analyze the spatial and temporal pattern of an event, and to (3) identify importance of events. In this demonstration, we show the overall system architecture, explain in detail the implementation of the components that crawl, classify, and rank tweets and extract location from tweets, and present some interesting results of our system.},
	booktitle = {2012 {IEEE} 28th {International} {Conference} on {Data} {Engineering}},
	author = {Li, R. and Lei, K. H. and Khadiwala, R. and Chang, K. C.},
	month = apr,
	year = {2012},
	keywords = {Conferences, Data engineering, TEDAS, Twitter-based event detection and analysis system, event spatial pattern, event temporal pattern, overall system architecture, social networking (online)},
	pages = {1273--1276}
}

@inproceedings{lin_extracting_2013,
	address = {New York, NY, USA},
	series = {{KDD} '13},
	title = {Extracting {Social} {Events} for {Learning} {Better} {Information} {Diffusion} {Models}},
	isbn = {978-1-4503-2174-7},
	url = {http://doi.acm.org/10.1145/2487575.2487584},
	doi = {10.1145/2487575.2487584},
	abstract = {Learning of the information diffusion model is a fundamental problem in the study of information diffusion in social networks. Existing approaches learn the diffusion models from events in social networks. However, events in social networks may have different underlying reasons. Some of them may be caused by the social influence inside the network, while others may reflect external trends in the ``real world''. Most existing work on the learning of diffusion models does not distinguish the events caused by the social influence from those caused by external trends. In this paper, we extract social events from data streams in social networks, and then use the extracted social events to improve the learning of information diffusion models. We propose a LADP (Latent Action Diffusion Path) model to incorporate the information diffusion model with the model of external trends, and then design an EM-based algorithm to infer the diffusion probabilities, the external trends and the sources of events efficiently.},
	urldate = {2018-10-08},
	booktitle = {Proceedings of the 19th {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}},
	publisher = {ACM},
	author = {Lin, Shuyang and Wang, Fengjiao and Hu, Qingbo and Yu, Philip S.},
	year = {2013},
	keywords = {information diffusion, social event, social influence},
	pages = {365--373}
}

@inproceedings{wu_who_2011,
	address = {Hyderabad, India},
	title = {Who says what to whom on twitter},
	isbn = {978-1-4503-0632-4},
	url = {http://portal.acm.org/citation.cfm?doid=1963405.1963504},
	doi = {10.1145/1963405.1963504},
	abstract = {We study several longstanding questions in media communications research, in the context of the microblogging service Twitter, regarding the production, ﬂow, and consumption of information. To do so, we exploit a recently introduced feature of Twitter known as “lists” to distinguish between elite users—by which we mean celebrities, bloggers, and representatives of media outlets and other formal organizations—and ordinary users. Based on this classiﬁcation, we ﬁnd a striking concentration of attention on Twitter, in that roughly 50\% of URLs consumed are generated by just 20K elite users, where the media produces the most information, but celebrities are the most followed. We also ﬁnd signiﬁcant homophily within categories: celebrities listen to celebrities, while bloggers listen to bloggers etc; however, bloggers in general rebroadcast more information than the other categories. Next we re-examine the classical “two-step ﬂow” theory of communications, ﬁnding considerable support for it on Twitter. Third, we ﬁnd that URLs broadcast by diﬀerent categories of users or containing diﬀerent types of content exhibit systematically diﬀerent lifespans. And ﬁnally, we examine the attention paid by the diﬀerent user categories to diﬀerent news topics.},
	language = {en},
	urldate = {2018-10-08},
	booktitle = {Proceedings of the 20th international conference on {World} wide web - {WWW} '11},
	publisher = {ACM Press},
	author = {Wu, Shaomei and Hofman, Jake M. and Mason, Winter A. and Watts, Duncan J.},
	year = {2011},
	pages = {705}
}

@inproceedings{kwak_what_2010,
	address = {Raleigh, North Carolina, USA},
	title = {What is {Twitter}, a social network or a news media?},
	isbn = {978-1-60558-799-8},
	url = {http://portal.acm.org/citation.cfm?doid=1772690.1772751},
	doi = {10.1145/1772690.1772751},
	abstract = {Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing.},
	language = {en},
	urldate = {2018-10-08},
	booktitle = {Proceedings of the 19th international conference on {World} wide web - {WWW} '10},
	publisher = {ACM Press},
	author = {Kwak, Haewoon and Lee, Changhyun and Park, Hosung and Moon, Sue},
	year = {2010},
	pages = {591}
}

@inproceedings{bakshy_everyones_2011,
	address = {Hong Kong, China},
	title = {Everyone's an influencer: quantifying influence on twitter},
	isbn = {978-1-4503-0493-1},
	shorttitle = {Everyone's an influencer},
	url = {http://portal.acm.org/citation.cfm?doid=1935826.1935845},
	doi = {10.1145/1935826.1935845},
	abstract = {In this paper we investigate the attributes and relative inﬂuence of 1.6M Twitter users by tracking 74 million diﬀusion events that took place on the Twitter follower graph over a two month interval in 2009. Unsurprisingly, we ﬁnd that the largest cascades tend to be generated by users who have been inﬂuential in the past and who have a large number of followers. We also ﬁnd that URLs that were rated more interesting and/or elicited more positive feelings by workers on Mechanical Turk were more likely to spread. In spite of these intuitive results, however, we ﬁnd that predictions of which particular user or URL will generate large cascades are relatively unreliable. We conclude, therefore, that wordof-mouth diﬀusion can only be harnessed reliably by targeting large numbers of potential inﬂuencers, thereby capturing average eﬀects. Finally, we consider a family of hypothetical marketing strategies, deﬁned by the relative cost of identifying versus compensating potential “inﬂuencers.” We ﬁnd that although under some circumstances, the most inﬂuential users are also the most cost-eﬀective, under a wide range of plausible assumptions the most cost-eﬀective performance can be realized using “ordinary inﬂuencers”—individuals who exert average or even less-than-average inﬂuence.},
	language = {en},
	urldate = {2018-10-08},
	booktitle = {Proceedings of the fourth {ACM} international conference on {Web} search and data mining - {WSDM} '11},
	publisher = {ACM Press},
	author = {Bakshy, Eytan and Hofman, Jake M. and Mason, Winter A. and Watts, Duncan J.},
	year = {2011},
	pages = {65}
}

@article{reuter_scalable_nodate,
	title = {Scalable {Event}-{Based} {Clustering} of {Social} {Media} {Via} {Record} {Linkage} {Techniques}},
	abstract = {We tackle the problem of grouping content available in social media applications such as Flickr, Youtube, Panoramino etc. into clusters of documents describing the same event. This task has been referred to as event identiﬁcation before. We present a new formalization of the event identiﬁcation task as a record linkage problem and show that this formulation leads to a principled and highly efﬁcient solution to the problem. We present results on two datasets derived from Flickr – last.fm and upcoming – comparing the results in terms of Normalized Mutual Information and F-Measure with respect to several baselines, showing that a record linkage approach outperforms all baselines as well as a stateof-the-art system. We demonstrate that our approach can scale to large amounts of data, reducing the processing time considerably compared to a state-of-theart approach. The scalability is achieved by applying an appropriate blocking strategy and relying on a Single Linkage clustering algorithm which avoids the exhaustive computation of pairwise similarities.},
	language = {en},
	author = {Reuter, Timo and Cimiano, Philipp and Drumond, Lucas and Buza, Krisztian and Schmidt-Thieme, Lars},
	pages = {8}
}

@inproceedings{sankaranarayanan_twitterstand:_2009,
	address = {Seattle, Washington},
	title = {{TwitterStand}: news in tweets},
	isbn = {978-1-60558-649-6},
	shorttitle = {{TwitterStand}},
	url = {http://portal.acm.org/citation.cfm?doid=1653771.1653781},
	doi = {10.1145/1653771.1653781},
	language = {en},
	urldate = {2018-10-08},
	booktitle = {Proceedings of the 17th {ACM} {SIGSPATIAL} {International} {Conference} on {Advances} in {Geographic} {Information} {Systems} - {GIS} '09},
	publisher = {ACM Press},
	author = {Sankaranarayanan, Jagan and Samet, Hanan and Teitler, Benjamin E. and Lieberman, Michael D. and Sperling, Jon},
	year = {2009},
	pages = {42}
}
@inproceedings{phuvipadawat_breaking_2010,
	title = {Breaking {News} {Detection} and {Tracking} in {Twitter}},
	volume = {3},
	doi = {10.1109/WI-IAT.2010.205},
	abstract = {Twitter has been used as one of the communication channels for spreading breaking news. We propose a method to collect, group, rank and track breaking news in Twitter. Since short length messages make similarity comparison difficult, we boost scores on proper nouns to improve the grouping results. Each group is ranked based on popularity and reliability factors. Current detection method is limited to facts part of messages. We developed an application called “Hotstream” based on the proposed method. Users can discover breaking news from the Twitter timeline. Each story is provided with the information of message originator, story development and activity chart. This provides a convenient way for people to follow breaking news and stay informed with real-time updates.},
	booktitle = {2010 {IEEE}/{WIC}/{ACM} {International} {Conference} on {Web} {Intelligence} and {Intelligent} {Agent} {Technology}},
	author = {Phuvipadawat, S. and Murata, T.},
	month = aug,
	year = {2010},
	keywords = {Feature extraction, Hotstream, Indexing, Information Retrieval, Nominations and elections, Real time systems, Real-time text-mining, Reliability, Topic Detection and Tracking, Twitter, User-generated content, breaking news detection, breaking news tracking, information retrieval, popularity factor, reliability factor, short length messages, social networking (online)},
	pages = {120--123}
}

@inproceedings{li_probabilistic_2005,
	title = {A probabilistic model for retrospective news event detection},
	abstract = {Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle.},
	booktitle = {In {Proceedings} of {SIGIR} ’05},
	author = {Li, Zhiwei and Li, Mingjing and Ma, Wei-ying},
	year = {2005},
	pages = {106--113}
}

@misc{noauthor_em-_nodate,
	title = {{EM}-最大期望算法},
	url = {http://www.csuldw.com/2015/12/02/2015-12-02-EM-algorithms/index.html},
	abstract = {对于EM算法，一直都是云里雾里。所以，今天索性就下个决定，不搞懂它，决不罢休。通过今天的学习，加上之前的基础，EM算法终于算是理清思绪了。回头想想，果真是如何做事不下定决心，真的很难有结果。下面，打算将EM算法的整个推导过程总结一遍，达到理解并掌握的目的。},
	urldate = {2018-10-08},
	journal = {D.W's Notes - Machine Learning}
}

@article{fathy_large-scale_2018,
	title = {Large-{Scale} {Indexing}, {Discovery}, and {Ranking} for the {Internet} of {Things} ({IoT})},
	volume = {51},
	issn = {0360-0300},
	url = {http://doi.acm.org/10.1145/3154525},
	doi = {10.1145/3154525},
	abstract = {Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data are usually multi-variant streams that are heterogeneous, sporadic, multi-modal, and spatio-temporal. IoT data can be disseminated with different granularities and have diverse structures, types, and qualities. Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery, and ranking mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data. However, the existing IoT data indexing and discovery approaches are complex or centralised, which hinders their scalability. The primary objective of this article is to provide a holistic overview of the state-of-the-art on indexing, discovery, and ranking of IoT data. The article aims to pave the way for researchers to design, develop, implement, and evaluate techniques and approaches for on-line large-scale distributed IoT applications and services.},
	number = {2},
	urldate = {2018-10-07},
	journal = {ACM Comput. Surv.},
	author = {Fathy, Yasmin and Barnaghi, Payam and Tafazolli, Rahim},
	month = mar,
	year = {2018},
	keywords = {Internet of things (IoT), discovery, indexing, large-scale data, ranking, wireless sensor network (WSN)},
	pages = {29:1--29:53}
}

@misc{noauthor_deep_2014,
	title = {A deep dive into {NoSQL}: {A} complete list of {NoSQL} databases},
	shorttitle = {A deep dive into {NoSQL}},
	url = {http://bigdata-madesimple.com/a-deep-dive-into-nosql-a-complete-list-of-nosql-databases/},
	abstract = {This is a complete list of NoSQL databases. A NoSQL database provides a mechanism for storage and retrieval of data.},
	language = {en-US},
	urldate = {2018-10-07},
	journal = {Big Data Made Simple - One source. Many perspectives.},
	month = jul,
	year = {2014}
}

@inproceedings{binta_adamu_survey_2015,
	title = {A {Survey} {On} {Big} {Data} {Indexing} {Strategies}},
	doi = {10.13140/RG.2.1.1844.0721},
	abstract = {The operations of the Internet have led to a significant growth and accumulation of data known as Big Data. Individuals and organizations that utilize this data, had no idea, nor were they prepared for this data explosion. Hence, the available solutions cannot meet the needs of the growing heterogeneous data in terms of processing. This results in inefficient information retrieval or search query results. The design of indexing strategies that can support this need is required. A survey on various indexing strategies and how they are utilized for solving Big Data management issues can serve as a guide for choosing the strategy best suited for a problem, and can also serve as a base for the design of more efficient indexing strategies. The aim of the study is to explore the characteristics of the indexing strategies used in Big Data manageability by covering some of the weaknesses and strengths of B-tree, R-tree, to name but a few. This paper covers some popular indexing strategies used for Big Data management. It exposes the potentials of each by carefully exploring their properties in ways that are related to problem solving.},
	author = {Binta Adamu, Fatima and Habbal, Adib and Hassan, Suhaidi and Cottrell, Les and White, Bebo and Abdullahi, Ibrahim},
	month = dec,
	year = {2015}
}

@article{adamu_survey_nodate,
	title = {A {Survey} {On} {Big} {Data} {Indexing} {Strategies}},
	abstract = {The operations of the Internet have led to a signiﬁcant growth and accumulation of data known as Big Data. Individuals and organizations that utilize this data, had no idea, nor were they prepared for this data explosion. Hence, the available solutions cannot meet the needs of the growing heterogeneous data in terms of processing. This results in inefﬁcient information retrieval or search query results. The design of indexing strategies that can support this need is required. A survey on various indexing strategies and how they are utilized for solving Big Data management issues can serve as a guide for choosing the strategy best suited for a problem, and can also serve as a base for the design of more efﬁcient indexing strategies. The aim of the study is to explore the characteristics of the indexing strategies used in Big Data manageability by covering some of the weaknesses and strengths of B-tree, R-tree, to name but a few. This paper covers some popular indexing strategies used for Big Data management. It exposes the potentials of each by carefully exploring their properties in ways that are related to problem solving.},
	language = {en},
	author = {Adamu, Fatima Binta and Habbal, Adib and Hassan, Suhaidi and Cottrell, R Les and White, Bebo and Abdullahi, Ibrahim},
	pages = {6}
}

@article{dittrich_efficiently_nodate,
	title = {Efficiently {Indexing} {AND} {Querying} {Big} {Data} in {Hadoop} {MapReduce}},
	language = {en},
	author = {Dittrich, Jens},
	pages = {30}
}

@inproceedings{shim_design_2018,
	series = {Lecture {Notes} in {Electrical} {Engineering}},
	title = {Design of {Effective} {Indexing} {Technique} in {Hadoop}-{Based} {Database}},
	isbn = {978-981-10-7605-3},
	abstract = {The recent rapid increase in the amount of data to be processed has led to the increased use of dispersed parallel processing of large-scale data analysis using open-source Hadoop’s MapReduce framework. The large-data processing method proposed by Google and Hadoop which implemented this are representative dispersed parallel processing methods, and the data are dispersedly saved on the HDFS(Hadoop Distributed File System). Such HDFS uses its own indexing technique when it comes to searching specific values from the saved files. Techniques that use conventional index, however, leads to problems like reduced search performance by not considering update and saving index in the disc. Therefore, the paper proposes effective DB indexing technique on Hadoop-based database.},
	language = {en},
	booktitle = {Advances in {Computer} {Science} and {Ubiquitous} {Computing}},
	publisher = {Springer Singapore},
	author = {Shim, Jae-Sung and Jang, Young-Hwan and Ju, Yong-Wan and Park, Seok-Cheon},
	editor = {Park, James J. and Loia, Vincenzo and Yi, Gangman and Sung, Yunsick},
	year = {2018},
	keywords = {B+-tree, BigData, DB index, Hadoop, Indexing technique},
	pages = {90--95}
}

@misc{rashkin_event2mind:_2018,
	title = {Event2Mind: {Commonsense} {Inference} on {Events}, {Intents}, and {Reactions}},
	shorttitle = {Event2Mind},
	url = {/paper/Event2Mind%3A-Commonsense-Inference-on-Events%2C-and-Rashkin-Sap/b89f8a9b2192a8f2018eead6b135ed30a1f2144d},
	abstract = {We investigate a new commonsense inference task: given an event described in a short free-form text (“X drinks coffee in the morning”), a system reasons about the likely intents (“X wants to stay awake”) and reactions (“X feels alert”) of the event’s participants. To support this study, we construct a new crowdsourced corpus of 25,000 event phrases covering a diverse range of everyday events and situations. We report baseline performance on this task, demonstrating that neural encoder-decoder models can successfully compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. In addition, we demonstrate how commonsense inference on people’s intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts.},
	language = {en},
	urldate = {2018-10-04},
	journal = {undefined},
	author = {Rashkin, Hannah and Sap, Maarten and Allaway, Emily and Smith, Noah A. and Choi, Yejin},
	year = {2018}
}

@inproceedings{stanovsky_supervised_2018,
	title = {Supervised {Open} {Information} {Extraction}},
	abstract = {We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a biLSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model, made publicly available,1 outperforms the state-of-the-art in Open IE on benchmark datasets.},
	booktitle = {{NAACL}-{HLT}},
	author = {Stanovsky, Gabriel and Michael, Julian and Zettlemoyer, Luke S. and Dagan, Ido},
	year = {2018},
	keywords = {Benchmark (computing), Information extraction, Semantic role labeling, Supervised learning, Transducer}
}

@article{dozat_deep_2016,
	title = {Deep {Biaffine} {Attention} for {Neural} {Dependency} {Parsing}},
	url = {http://arxiv.org/abs/1611.01734},
	abstract = {This paper builds off recent work from Kiperwasser \& Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on standard treebanks for six different languages, achieving 95.7\% UAS and 94.1\% LAS on the most popular English PTB dataset. This makes it the highest-performing graph-based parser on this benchmark---outperforming Kiperwasser Goldberg (2016) by 1.8\% and 2.2\%---and comparable to the highest performing transition-based parser (Kuncoro et al., 2016), which achieves 95.8\% UAS and 94.6\% LAS. We also show which hyperparameter choices had a significant effect on parsing accuracy, allowing us to achieve large gains over other graph-based approaches.},
	urldate = {2018-10-04},
	journal = {arXiv:1611.01734 [cs]},
	author = {Dozat, Timothy and Manning, Christopher D.},
	month = nov,
	year = {2016},
	note = {arXiv: 1611.01734},
	keywords = {Computer Science - Computation and Language, Computer Science - Neural and Evolutionary Computing}
}

@article{joshi_extending_2018,
	title = {Extending a {Parser} to {Distant} {Domains} {Using} a {Few} {Dozen} {Partially} {Annotated} {Examples}},
	url = {http://arxiv.org/abs/1805.06556},
	abstract = {We revisit domain adaptation for parsers in the neural era. First we show that recent advances in word representations greatly diminish the need for domain adaptation when the target domain is syntactically similar to the source domain. As evidence, we train a parser on the Wall Street Jour- nal alone that achieves over 90\% F1 on the Brown corpus. For more syntactically dis- tant domains, we provide a simple way to adapt a parser using only dozens of partial annotations. For instance, we increase the percentage of error-free geometry-domain parses in a held-out set from 45\% to 73\% using approximately five dozen training examples. In the process, we demon- strate a new state-of-the-art single model result on the Wall Street Journal test set of 94.3\%. This is an absolute increase of 1.7\% over the previous state-of-the-art of 92.6\%.},
	urldate = {2018-10-04},
	journal = {arXiv:1805.06556 [cs]},
	author = {Joshi, Vidur and Peters, Matthew and Hopkins, Mark},
	month = may,
	year = {2018},
	note = {arXiv: 1805.06556},
	keywords = {Computer Science - Computation and Language}
}

@misc{peters_semi-supervised_2017,
	title = {Semi-supervised sequence tagging with bidirectional language models},
	url = {/paper/Semi-supervised-sequence-tagging-with-bidirectional-Peters-Ammar/85d82c7c5cca04785e0c02b84bf3e6d8703b9d09},
	abstract = {Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data. In this paper, we demonstrate a general semi-supervised approach for adding pretrained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks. We evaluate our model on two standard datasets for named entity recognition (NER) and chunking, and in both cases achieve state of the art results, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers.},
	language = {en},
	urldate = {2018-10-04},
	journal = {undefined},
	author = {Peters, Matthew E. and Ammar, Waleed and Bhagavatula, Chandra and Power, Russell},
	year = {2017}
}

@misc{lee_end--end_2017,
	title = {End-to-end {Neural} {Coreference} {Resolution}},
	url = {/paper/End-to-end-Neural-Coreference-Resolution-Lee-He/8ae1af4a424f5e464d46903bc3d18fe1cf1434ff},
	abstract = {We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or handengineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a headfinding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.},
	language = {en},
	urldate = {2018-10-04},
	journal = {undefined},
	author = {Lee, Kenton and He, Luheng and Lewis, Mike and Zettlemoyer, Luke S.},
	year = {2017}
}

@inproceedings{he_deep_2017,
	title = {Deep {Semantic} {Role} {Labeling}: {What} {Works} and {What}'s {Next}},
	shorttitle = {Deep {Semantic} {Role} {Labeling}},
	doi = {10.18653/v1/P17-1044},
	abstract = {We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent best practices for initialization and regularization. Our 8-layer ensemble model achieves 83.2 F1 on the CoNLL 2005 test set and 83.4 F1 on CoNLL 2012, roughly a 10\% relative error reduction over the previous state of the art. Extensive empirical analysis of these gains show that (1) deep models excel at recovering long-distance dependencies but can still make surprisingly obvious errors, and (2) that there is still room for syntactic parsers to improve these results.},
	booktitle = {{ACL}},
	author = {He, Luheng and Lee, Kenton and Lewis, Mike and Zettlemoyer, Luke S.},
	year = {2017},
	keywords = {Approximation error, Best practice, Deep learning, Parsing, Semantic role labeling, Test set}
}

@misc{parikh_decomposable_2016,
	title = {A {Decomposable} {Attention} {Model} for {Natural} {Language} {Inference}},
	url = {/paper/A-Decomposable-Attention-Model-for-Natural-Language-Parikh-T%C3%A4ckstr%C3%B6m/07a9478e87a8304fc3267fa16e83e9f3bbd98b27},
	abstract = {We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements.},
	language = {en},
	urldate = {2018-10-04},
	journal = {undefined},
	author = {Parikh, Ankur P. and Täckström, Oscar and Das, Dipanjan and Uszkoreit, Jakob},
	year = {2016}
}

@inproceedings{xiong_context_2017,
	title = {Context and {Context} 2 {Query} {Attention} {Word} {Embedding} {GLOVE}},
	abstract = {Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query. Recently, attention mechanisms have been successfully extended to MC. Typically these methods use attention to focus on a small portion of the context and summarize it with a fixed-size vector, couple attentions temporally, and/or often form a uni-directional attention. In this paper we introduce the Bi-Directional Attention Flow (BIDAF) network, a multi-stage hierarchical process that represents the context at different levels of granularity and uses bidirectional attention flow mechanism to obtain a query-aware context representation without early summarization. Our experimental evaluations show that our model achieves the state-of-the-art results in Stanford Question Answering Dataset (SQuAD) and CNN/DailyMail cloze test.},
	author = {Xiong, Caiming and Zhong, Victor and Yang, Zhilin and Dhingra, Bhuwan and Yuan, Ye and Hu, Junjie and Cohen, William W. and Yang, Zichao and He, Xiaodong and Gao, Jianfeng and Deng, Li and Smola, Alex and Bernstein, Michael S. and Fei-Fei, Li},
	year = {2017},
	keywords = {Bi-directional text, Interaction, List comprehension, Temporal logic}
}

@article{see_get_2017,
	title = {Get {To} {The} {Point}: {Summarization} with {Pointer}-{Generator} {Networks}},
	shorttitle = {Get {To} {The} {Point}},
	url = {http://arxiv.org/abs/1704.04368},
	abstract = {Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.},
	urldate = {2018-09-26},
	journal = {arXiv:1704.04368 [cs]},
	author = {See, Abigail and Liu, Peter J. and Manning, Christopher D.},
	month = apr,
	year = {2017},
	note = {arXiv: 1704.04368},
	keywords = {Computer Science - Computation and Language}
}

@article{liu_learning_2007,
	title = {Learning to {Rank} for {Information} {Retrieval}},
	volume = {3},
	issn = {1554-0669, 1554-0677},
	url = {http://www.nowpublishers.com/article/Details/INR-016},
	doi = {10.1561/1500000016},
	abstract = {Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Speciﬁcally, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches. The advantages and disadvantages with each approach are analyzed, and the relationships between the loss functions used in these approaches and IR evaluation measures are discussed. Then the empirical evaluations on typical learning-to-rank methods are shown, with the LETOR collection as a benchmark dataset, which seems to suggest that the listwise approach be the most eﬀective one among all the approaches. After that, a statistical ranking theory is introduced, which can describe different learning-to-rank algorithms, and be used to analyze their querylevel generalization abilities. At the end of the tutorial, we provide a summary and discuss potential future work on learning to rank.},
	language = {en},
	number = {3},
	urldate = {2018-09-25},
	journal = {Foundations and Trends® in Information Retrieval},
	author = {Liu, Tie-Yan},
	year = {2007},
	pages = {225--331}
}

@article{nakamoto_foreword_2011,
	title = {{FOREWORD}},
	volume = {E94-D},
	issn = {0916-8532, 1745-1361},
	url = {http://joi.jlc.jst.go.jp/JST.JSTAGE/transinf/E94.D.1?from=CrossRef},
	doi = {10.1587/transinf.E94.D.1},
	abstract = {Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in Information Retrieval, Natural Language Processing, and Data Mining. Intensive studies have been conducted on the problem and signiﬁcant progress has been made [1], [2]. This short paper gives an introduction to learning to rank, and it speciﬁcally explains the fundamental problems, existing approaches, and future work of learning to rank. Several learning to rank methods using SVM techniques are described in details.},
	language = {en},
	number = {1},
	urldate = {2018-09-25},
	journal = {IEICE Transactions on Information and Systems},
	author = {Nakamoto, Yukikazu},
	year = {2011},
	pages = {1--2}
}

@inproceedings{lavrenko_relevance_2001,
	address = {New York, NY, USA},
	series = {{SIGIR} '01},
	title = {Relevance {Based} {Language} {Models}},
	isbn = {978-1-58113-331-8},
	url = {http://doi.acm.org/10.1145/383952.383972},
	doi = {10.1145/383952.383972},
	abstract = {We explore the relation between classical probabilistic models of information retrieval and the emerging language modeling approaches. It has long been recognized that the primary obstacle to effective performance of classical models is the need to estimate arelevance model: probabilities of words in the relevant class. We propose a novel technique for estimating these probabilities using the query alone. We demonstrate that our technique can produce highly accurate relevance models, addressing important notions of synonymy and polysemy. Our experiments show relevance models outperforming baseline language modeling systems on TREC retrieval and TDT tracking tasks. The main contribution of this work is an effective formal method for estimating a relevance model with no training data.},
	urldate = {2018-09-25},
	booktitle = {Proceedings of the 24th {Annual} {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {ACM},
	author = {Lavrenko, Victor and Croft, W. Bruce},
	year = {2001},
	pages = {120--127}
}

@inproceedings{lv_positional_2009,
	address = {Boston, MA, USA},
	title = {Positional language models for information retrieval},
	isbn = {978-1-60558-483-6},
	url = {http://portal.acm.org/citation.cfm?doid=1571941.1571994},
	doi = {10.1145/1571941.1571994},
	abstract = {Although many variants of language models have been proposed for information retrieval, there are two related retrieval heuristics remaining “external” to the language modeling approach: (1) proximity heuristic which rewards a document where the matched query terms occur close to each other; (2) passage retrieval which scores a document mainly based on the best matching passage. Existing studies have only attempted to use a standard language model as a “black box” to implement these heuristics, making it hard to optimize the combination parameters.},
	language = {en},
	urldate = {2018-09-25},
	booktitle = {Proceedings of the 32nd international {ACM} {SIGIR} conference on {Research} and development in information retrieval - {SIGIR} '09},
	publisher = {ACM Press},
	author = {Lv, Yuanhua and Zhai, ChengXiang},
	year = {2009},
	pages = {299}
}

@inproceedings{metzler_markov_2005,
	address = {Salvador, Brazil},
	title = {A {Markov} random field model for term dependencies},
	isbn = {978-1-59593-034-7},
	url = {http://dl.acm.org/citation.cfm?doid=1076034.1076115},
	doi = {10.1145/1076034.1076115},
	abstract = {This paper develops a general, formal framework for modeling term dependencies via Markov random ﬁelds. The model allows for arbitrary text features to be incorporated as evidence. In particular, we make use of features based on occurrences of single terms, ordered phrases, and unordered phrases. We explore full independence, sequential dependence, and full dependence variants of the model. A novel approach is developed to train the model that directly maximizes the mean average precision rather than maximizing the likelihood of the training data. Ad hoc retrieval experiments are presented on several newswire and web collections, including the GOV2 collection used at the TREC 2004 Terabyte Track. The results show signiﬁcant improvements are possible by modeling dependencies, especially on the larger web collections.},
	language = {en},
	urldate = {2018-09-25},
	booktitle = {Proceedings of the 28th annual international {ACM} {SIGIR} conference on {Research} and development in information retrieval - {SIGIR} '05},
	publisher = {ACM Press},
	author = {Metzler, Donald and Croft, W. Bruce},
	year = {2005},
	pages = {472}
}

@inproceedings{robertson_simple_2004,
	address = {Washington, D.C., USA},
	title = {Simple {BM}25 extension to multiple weighted fields},
	isbn = {978-1-58113-874-0},
	url = {http://portal.acm.org/citation.cfm?doid=1031171.1031181},
	doi = {10.1145/1031171.1031181},
	language = {en},
	urldate = {2018-09-25},
	booktitle = {Proceedings of the {Thirteenth} {ACM} conference on {Information} and knowledge management  - {CIKM} '04},
	publisher = {ACM Press},
	author = {Robertson, Stephen and Zaragoza, Hugo and Taylor, Michael},
	year = {2004},
	pages = {42}
}

@article{deerwester_indexing_1990,
	title = {Indexing by latent semantic analysis},
	volume = {41},
	issn = {0002-8231, 1097-4571},
	url = {http://doi.wiley.com/10.1002/%28SICI%291097-4571%28199009%2941%3A6%3C391%3A%3AAID-ASI1%3E3.0.CO%3B2-9},
	doi = {10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9},
	language = {en},
	number = {6},
	urldate = {2018-09-25},
	journal = {Journal of the American Society for Information Science},
	author = {Deerwester, Scott and Dumais, Susan T. and Furnas, George W. and Landauer, Thomas K. and Harshman, Richard},
	month = sep,
	year = {1990},
	pages = {391--407}
}

@inproceedings{lin_global_2018,
	address = {Melbourne, Australia},
	title = {Global {Encoding} for {Abstractive} {Summarization}},
	url = {http://www.aclweb.org/anthology/P18-2027},
	abstract = {In neural abstractive summarization, the conventional sequence-to-sequence (seq2seq) model often suffers from repetition and semantic irrelevance. To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context. It consists of a convolutional gated unit to perform global encoding to improve the representations of the source-side information. Evaluations on the LCSTS and the English Gigaword both demonstrate that our model outperforms the baseline models, and the analysis shows that our model is capable of generating summary of higher quality and reducing repetition.},
	urldate = {2018-09-24},
	booktitle = {Proceedings of the 56th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({Volume} 2: {Short} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Lin, Junyang and SUN, Xu and Ma, Shuming and Su, Qi},
	month = jul,
	year = {2018},
	pages = {163--169}
}

@article{gehrmann_bottom-up_2018,
	title = {Bottom-{Up} {Abstractive} {Summarization}},
	url = {http://arxiv.org/abs/1808.10792},
	abstract = {Neural network-based methods for abstractive summarization produce outputs that are more fluent than other techniques, but which can be poor at content selection. This work proposes a simple technique for addressing this issue: use a data-efficient content selector to over-determine phrases in a source document that should be part of the summary. We use this selector as a bottom-up attention step to constrain the model to likely phrases. We show that this approach improves the ability to compress text, while still generating fluent summaries. This two-step process is both simpler and higher performing than other end-to-end content selection models, leading to significant improvements on ROUGE for both the CNN-DM and NYT corpus. Furthermore, the content selector can be trained with as little as 1,000 sentences, making it easy to transfer a trained summarizer to a new domain.},
	urldate = {2018-09-24},
	journal = {arXiv:1808.10792 [cs]},
	author = {Gehrmann, Sebastian and Deng, Yuntian and Rush, Alexander M.},
	month = aug,
	year = {2018},
	note = {arXiv: 1808.10792},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning}
}

@article{chen_fast_2018,
	title = {Fast {Abstractive} {Summarization} with {Reinforce}-{Selected} {Sentence} {Rewriting}},
	url = {https://arxiv.org/abs/1805.11080},
	language = {en},
	urldate = {2018-09-24},
	author = {Chen, Yen-Chun and Bansal, Mohit},
	month = may,
	year = {2018}
}

@article{paulus_deep_2017,
	title = {A {Deep} {Reinforced} {Model} for {Abstractive} {Summarization}},
	url = {https://arxiv.org/abs/1705.04304},
	language = {en},
	urldate = {2018-09-24},
	author = {Paulus, Romain and Xiong, Caiming and Socher, Richard},
	month = may,
	year = {2017}
}

@inproceedings{nallapati_abstractive_2016,
	address = {Berlin, Germany},
	title = {Abstractive {Text} {Summarization} using {Sequence}-to-sequence {RNNs} and {Beyond}},
	url = {http://www.aclweb.org/anthology/K16-1028},
	urldate = {2018-09-24},
	booktitle = {Proceedings of {The} 20th {SIGNLL} {Conference} on {Computational} {Natural} {Language} {Learning}},
	publisher = {Association for Computational Linguistics},
	author = {Nallapati, Ramesh and Zhou, Bowen and dos Santos, Cicero and Gulcehre, Caglar and Xiang, Bing},
	month = aug,
	year = {2016},
	pages = {280--290}
}

@article{khatri_abstractive_2018,
	title = {Abstractive and {Extractive} {Text} {Summarization} using {Document} {Context} {Vector} and {Recurrent} {Neural} {Networks}},
	url = {http://arxiv.org/abs/1807.08000},
	abstract = {Sequence to sequence (Seq2Seq) learning has recently been used for abstractive and extractive summarization. In current study, Seq2Seq models have been used for eBay product description summarization. We propose a novel Document-Context based Seq2Seq models using RNNs for abstractive and extractive summarizations. Intuitively, this is similar to humans reading the title, abstract or any other contextual information before reading the document. This gives humans a high-level idea of what the document is about. We use this idea and propose that Seq2Seq models should be started with contextual information at the first time-step of the input to obtain better summaries. In this manner, the output summaries are more document centric, than being generic, overcoming one of the major hurdles of using generative models. We generate document-context from user-behavior and seller provided information. We train and evaluate our models on human-extracted-golden-summaries. The document-contextual Seq2Seq models outperform standard Seq2Seq models. Moreover, generating human extracted summaries is prohibitively expensive to scale, we therefore propose a semi-supervised technique for extracting approximate summaries and using it for training Seq2Seq models at scale. Semi-supervised models are evaluated against human extracted summaries and are found to be of similar efficacy. We provide side by side comparison for abstractive and extractive summarizers (contextual and non-contextual) on same evaluation dataset. Overall, we provide methodologies to use and evaluate the proposed techniques for large document summarization. Furthermore, we found these techniques to be highly effective, which is not the case with existing techniques.},
	urldate = {2018-09-24},
	journal = {arXiv:1807.08000 [cs]},
	author = {Khatri, Chandra and Singh, Gyanit and Parikh, Nish},
	month = jul,
	year = {2018},
	note = {arXiv: 1807.08000},
	keywords = {Computer Science - Computation and Language}
}

@article{see_get_2017-1,
	title = {Get {To} {The} {Point}: {Summarization} with {Pointer}-{Generator} {Networks}},
	shorttitle = {Get {To} {The} {Point}},
	url = {http://arxiv.org/abs/1704.04368},
	abstract = {Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.},
	urldate = {2018-09-24},
	journal = {arXiv:1704.04368 [cs]},
	author = {See, Abigail and Liu, Peter J. and Manning, Christopher D.},
	month = apr,
	year = {2017},
	note = {arXiv: 1704.04368},
	keywords = {Computer Science - Computation and Language}
}

@inproceedings{hingu_automatic_2015,
	title = {Automatic text summarization of {Wikipedia} articles},
	doi = {10.1109/ICCICT.2015.7045732},
	abstract = {The main objective of a text summarization system is to identify the most important information from the given text and present it to the end users. In this paper, Wikipedia articles are given as input to system and extractive text summarization is presented by identifying text features and scoring the sentences accordingly. The text is first pre-processed to tokenize the sentences and perform stemming operations. We then score the sentences using the different text features. Two novel approaches implemented are using the citations present in the text and identifying synonyms. These features along with the traditional methods are used to score the sentences. The scores are used to classify the sentence to be in the summary text or not with the help of a neural network. The user can provide what percentage of the original text should be in the summary. It is found that scoring the sentences based on citations gives the best results.},
	booktitle = {2015 {International} {Conference} on {Communication}, {Information} {Computing} {Technology} ({ICCICT})},
	author = {Hingu, D. and Shah, D. and Udmale, S. S.},
	month = jan,
	year = {2015},
	keywords = {Computers, Electronic publishing, Encyclopedias, Feature extraction, Frequency, Internet, Natural Language, Neural networks, Python, Text summarization, Web sites, Wikipedia articles, automatic text summarization, neural nets, neural network, sentence classification, sentence scoring, sentence tokenization, stemming operations, text analysis, text feature identification, text preprocessing},
	pages = {1--4}
}

@article{paulus_deep_2017-1,
	title = {A {Deep} {Reinforced} {Model} for {Abstractive} {Summarization}},
	url = {http://arxiv.org/abs/1705.04304},
	abstract = {Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit "exposure bias" - they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN/Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN/Daily Mail dataset, an improvement over previous state-of-the-art models. Human evaluation also shows that our model produces higher quality summaries.},
	urldate = {2018-06-16},
	journal = {arXiv:1705.04304 [cs]},
	author = {Paulus, Romain and Xiong, Caiming and Socher, Richard},
	month = may,
	year = {2017},
	note = {arXiv: 1705.04304},
	keywords = {Computer Science - Computation and Language}
}

@inproceedings{rangarajan_sridhar_unsupervised_2015,
	title = {Unsupervised {Topic} {Modeling} for {Short} {Texts} {Using} {Distributed} {Representations} of {Words}},
	url = {http://aclweb.org/anthology/W15-1526},
	doi = {10.3115/v1/W15-1526},
	abstract = {We present an unsupervised topic model for short texts that performs soft clustering over distributed representations of words. We model the low-dimensional semantic vector space represented by the dense distributed representations of words using Gaussian mixture models (GMMs) whose components capture the notion of latent topics. While conventional topic modeling schemes such as probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA) need aggregation of short messages to avoid data sparsity in short documents, our framework works on large amounts of raw short texts (billions of words). In contrast with other topic modeling frameworks that use word cooccurrence statistics, our framework uses a vector space model that overcomes the issue of sparse word co-occurrence patterns. We demonstrate that our framework outperforms LDA on short texts through both subjective and objective evaluation. We also show the utility of our framework in learning topics and classifying short texts on Twitter data for English, Spanish, French, Portuguese and Russian.},
	language = {en},
	urldate = {2018-06-15},
	publisher = {Association for Computational Linguistics},
	author = {Rangarajan Sridhar, Vivek Kumar},
	year = {2015},
	pages = {192--200}
}

@inproceedings{xun_topic_2016,
	title = {Topic {Discovery} for {Short} {Texts} {Using} {Word} {Embeddings}},
	doi = {10.1109/ICDM.2016.0176},
	abstract = {Discovering topics in short texts, such as news titles and tweets, has become an important task for many content analysis applications. However, due to the lack of rich context information in short texts, the performance of conventional topic models on short texts is usually unsatisfying. In this paper, we propose a novel topic model for short text corpus using word embeddings. Continuous space word embeddings, which is proven effective at capturing regularities in language, is incorporated into our model to provide additional semantics. Thus we model each short document as a Gaussian topic over word embeddings in the vector space. In addition, considering that background words in a short text are usually not semantically related, we introduce a discrete background mode over word types to complement the continuous Gaussian topics. We evaluate our model on news titles from data sources like abcnews, showing that our model is able to extract more coherent topics from short texts compared with the baseline methods and learn better topic representation for each short document.},
	booktitle = {2016 {IEEE} 16th {International} {Conference} on {Data} {Mining} ({ICDM})},
	author = {Xun, G. and Gopalakrishnan, V. and Ma, F. and Li, Y. and Gao, J. and Zhang, A.},
	month = dec,
	year = {2016},
	keywords = {Context, Electronic publishing, Encyclopedias, Gaussian distribution, Gaussian processes, Gaussian topic, Internet, Semantics, abcnews, baseline methods, content analysis applications, continuous space word embeddings, data sources, discrete background mode, news titles, short text corpus, short texts, text analysis, topic discovery, topic model, tweets, vector space, vectors, word embeddings},
	pages = {1299--1304}
}

@article{zuo_word_2014,
	title = {Word {Network} {Topic} {Model}: {A} {Simple} but {General} {Solution} for {Short} and {Imbalanced} {Texts}},
	shorttitle = {Word {Network} {Topic} {Model}},
	url = {https://arxiv.org/abs/1412.5404},
	language = {en},
	urldate = {2018-06-15},
	author = {Zuo, Yuan and Zhao, Jichang and Xu, Ke},
	month = dec,
	year = {2014}
}

@article{hajjem_combining_2017,
	series = {Knowledge-{Based} and {Intelligent} {Information} \& {Engineering} {Systems}: {Proceedings} of the 21st {International} {Conference}, {KES}-20176-8 {September} 2017, {Marseille}, {France}},
	title = {Combining {IR} and {LDA} {Topic} {Modeling} for {Filtering} {Microblogs}},
	volume = {112},
	issn = {1877-0509},
	url = {http://www.sciencedirect.com/science/article/pii/S1877050917315235},
	doi = {10.1016/j.procs.2017.08.166},
	abstract = {Twitter is a networking micro-blogging service where users post millions of short messages every day. Building multilingual corpora from these microblogs contents can be useful to perform several computational tasks such as opinion mining. However, Twitter data gathering involves the problem of irrelevant included data. Recent literary works have proved that topic models such as Latent Dirichlet Allocation (LDA) are not consistent when applied to short texts like tweets. In order to prune the irrelevant tweets, we investigate in this paper a novel method to improve topics learned from Twitter content without modifying the basic machinery of LDA. This latter is based on a pooling process which combines Information retrieval (IR) approach and LDA.This is achieved through an aggregation strategy based on IR task to retrieve similar tweets in a same cluster. The result of tweet pooling is then used as an input for a basic LDA to overcome the sparsity problem of Twitter content. Empirical results highlight that tweets aggregation based on IR and LDA leads to an interesting improvement in a variety of measures for topic coherence, in comparison to unmodified LDA baseline and a variety of pooling schemes.},
	urldate = {2018-06-15},
	journal = {Procedia Computer Science},
	author = {Hajjem, Malek and Latiri, Chiraz},
	month = jan,
	year = {2017},
	keywords = {Aggregation, Information Retrieval, LDA, Microblogs, Pruning irrelevant tweets},
	pages = {761--770}
}
@inproceedings{konrad_2d--3d_2012,
	title = {2D-to-3D image conversion by learning depth from examples},
	doi = {10.1109/CVPRW.2012.6238903},
	abstract = {Among 2D-to-3D image conversion methods, those involving human operators have been most successful but also time-consuming and costly. Automatic methods, that typically make use of a deterministic 3D scene model, have not yet achieved the same level of quality as they often rely on assumptions that are easily violated in practice. In this paper, we adopt the radically different approach of “learning” the 3D scene structure. We develop a simplified and computationally-efficient version of our recent 2D-to-3D image conversion algorithm. Given a repository of 3D images, either as stereopairs or image+depth pairs, we find k pairs whose photometric content most closely matches that of a 2D query to be converted. Then, we fuse the k corresponding depth fields and align the fused depth with the 2D query. Unlike in our original work, we validate the simplified algorithm quantitatively on a Kinect-captured image+depth dataset against the Make3D algorithm. While far from perfect, the presented results demonstrate that online repositories of 3D content can be used for effective 2D-to-3D image conversion.},
	booktitle = {2012 {IEEE} {Computer} {Society} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} {Workshops}},
	author = {Konrad, J. and Wang, M. and Ishwar, P.},
	month = jun,
	year = {2012},
	keywords = {2D query conversion, 2D-to-3D image conversion algorithm, 3D image repository, 3D scene structure learning, Cameras, Databases, Dictionaries, Humans, Image edge detection, Kinect-captured image plus depth dataset, Measurement, Smoothing methods, automatic method, depth fields, deterministic 3D scene model, fused depth, image fusion, image plus depth pairs, image quality, image retrieval, photometric content, solid modelling, stereo image processing, stereopairs},
	pages = {16--22}
}

@inproceedings{tong_dynamic_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Dynamic {Pricing} in {Spatial} {Crowdsourcing}: {A} {Matching}-{Based} {Approach}},
	isbn = {978-1-4503-4703-7},
	shorttitle = {Dynamic {Pricing} in {Spatial} {Crowdsourcing}},
	url = {http://doi.acm.org/10.1145/3183713.3196929},
	doi = {10.1145/3183713.3196929},
	abstract = {In spatial crowdsourcing, requesters submit their task-related locations and increase the demand of a local area. The platform prices these tasks and assigns spatial workers to serve if the prices are accepted by requesters. There exist mature pricing strategies which specialize in tackling the imbalance between supply and demand in a local market. However, in global optimization, the platform should consider the mobility of workers; that is, any single worker can be the potential supply for several areas, while it can only be the true supply of one area when assigned by the platform. The hardness lies in the uncertainty of the true supply of each area, hence the existing pricing strategies do not work. In the paper, we formally define this {\textless}u{\textgreater}G{\textless}/u{\textgreater}lobal {\textless}u{\textgreater}D{\textless}/u{\textgreater}ynamic {\textless}u{\textgreater}P{\textless}/u{\textgreater}ricing(GDP) problem in spatial crowdsourcing. And since the objective is concerned with how the platform matches the supply to areas, we let the matching algorithm guide us how to price. We propose a {\textless}u{\textgreater}MA{\textless}/u{\textgreater}tching-based {\textless}u{\textgreater}P{\textless}/u{\textgreater}ricing {\textless}u{\textgreater}S{\textless}/u{\textgreater}trategy (MAPS) with guaranteed bound. Extensive experiments conducted on the synthetic and real datasets demonstrate the effectiveness of MAPS.},
	urldate = {2018-06-06},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Tong, Yongxin and Wang, Libin and Zhou, Zimu and Chen, Lei and Du, Bowen and Ye, Jieping},
	year = {2018},
	keywords = {pricing strategy, spatial crowdsourcing},
	pages = {773--788}
}

@inproceedings{tai_sketching_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Sketching {Linear} {Classifiers} over {Data} {Streams}},
	isbn = {978-1-4503-4703-7},
	url = {http://doi.acm.org/10.1145/3183713.3196930},
	doi = {10.1145/3183713.3196930},
	abstract = {We introduce a new sub-linear space sketch---the Weight-Median Sketch---for learning compressed linear classifiers over data streams while supporting the efficient recovery of large-magnitude weights in the model. This enables memory-limited execution of several statistical analyses over streams, including online feature selection, streaming data explanation, relative deltoid detection, and streaming estimation of pointwise mutual information. Unlike related sketches that capture the most frequently-occurring features (or items) in a data stream, the Weight-Median Sketch captures the features that are most discriminative of one stream (or class) compared to another. The Weight-Median Sketch adopts the core data structure used in the Count-Sketch, but, instead of sketching counts, it captures sketched gradient updates to the model parameters. We provide a theoretical analysis that establishes recovery guarantees for batch and online learning, and demonstrate empirical improvements in memory-accuracy trade-offs over alternative memory-budgeted methods, including count-based sketches and feature hashing.},
	urldate = {2018-06-06},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Tai, Kai Sheng and Sharan, Vatsal and Bailis, Peter and Valiant, Gregory},
	year = {2018},
	keywords = {linear classification, online learning, sketching},
	pages = {757--772}
}

@inproceedings{armbrust_structured_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Structured {Streaming}: {A} {Declarative} {API} for {Real}-{Time} {Applications} in {Apache} {Spark}},
	isbn = {978-1-4503-4703-7},
	shorttitle = {Structured {Streaming}},
	url = {http://doi.acm.org/10.1145/3183713.3190664},
	doi = {10.1145/3183713.3190664},
	abstract = {With the ubiquity of real-time data, organizations need streaming systems that are scalable, easy to use, and easy to integrate into business applications. Structured Streaming is a new high-level streaming API in Apache Spark based on our experience with Spark Streaming. Structured Streaming differs from other recent streaming APIs, such as Google Dataflow, in two main ways. First, it is a purely declarative API based on automatically incrementalizing a static relational query (expressed using SQL or DataFrames), in contrast to APIs that ask the user to build a DAG of physical operators. Second, Structured Streaming aims to support end-to-end real-time applications that integrate streaming with batch and interactive analysis. We found that this integration was often a key challenge in practice. Structured Streaming achieves high performance via Spark SQL's code generation engine and can outperform Apache Flink by up to 2x and Apache Kafka Streams by 90x. It also offers rich operational features such as rollbacks, code updates, and mixed streaming/batch execution. We describe the system's design and use cases from several hundred production deployments on Databricks, the largest of which process over 1 PB of data per month.},
	urldate = {2018-06-04},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Armbrust, Michael and Das, Tathagata and Torres, Joseph and Yavuz, Burak and Zhu, Shixiong and Xin, Reynold and Ghodsi, Ali and Stoica, Ion and Zaharia, Matei},
	year = {2018},
	keywords = {apache spark, programming models, stream processing},
	pages = {601--613}
}

@inproceedings{yang_robust_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Robust, {Scalable}, {Real}-{Time} {Event} {Time} {Series} {Aggregation} at {Twitter}},
	isbn = {978-1-4503-4703-7},
	url = {http://doi.acm.org/10.1145/3183713.3190663},
	doi = {10.1145/3183713.3190663},
	abstract = {Twitter's data engineering team is faced with the challenge of processing billions of events every day in batch and in real time, and we have built various tools to meet these demands. In this paper, we describe TSAR (TimeSeries AggregatoR), a robust, scalable, real-time event time series aggregation framework built primarily for engagement monitoring: aggregating interactions with Tweets, segmented along a multitude of dimensions such as device, engagement type, etc. TSAR is built on top of Summingbird, an open-source framework for integrating batch and online MapReduce computations, and removes much of the tedium associated with building end-to-end aggregation pipelines---from the ingestion and processing of events to the publication of results in heterogeneous datastores. Clients are provided a query interface that powers dashboards and supports downstream ad hoc analytics.},
	urldate = {2018-06-04},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Yang, Peilin and Thiagarajan, Srikanth and Lin, Jimmy},
	year = {2018},
	keywords = {batch processing, online processing},
	pages = {595--599}
}

@inproceedings{im_pinot:_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Pinot: {Realtime} {OLAP} for 530 {Million} {Users}},
	isbn = {978-1-4503-4703-7},
	shorttitle = {Pinot},
	url = {http://doi.acm.org/10.1145/3183713.3190661},
	doi = {10.1145/3183713.3190661},
	abstract = {Modern users demand analytical features on fresh, real time data. Offering these analytical features to hundreds of millions of users is a relevant problem encountered by many large scale web companies. Relational databases and key-value stores can be scaled to provide point lookups for a large number of users but fall apart at the combination of high ingest rates, high query rates at low latency for analytical queries. Online analytical databases typically rely on bulk data loads and are not typically built to handle nonstop operation in demanding web environments. Offline analytical systems have high throughput but do not offer low query latencies nor can scale to serving tens of thousands of queries per second. We present Pinot, a single system used in production at Linkedin that can serve tens of thousands of analytical queries per second, offers near-realtime data ingestion from streaming data sources, and handles the operational requirements of large web properties. We also provide a performance comparison with Druid, a system similar to Pinot.},
	urldate = {2018-06-04},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Im, Jean-François and Gopalakrishna, Kishore and Subramaniam, Subbu and Shrivastava, Mayank and Tumbde, Adwait and Jiang, Xiaotian and Dai, Jennifer and Lee, Seunghyun and Pawar, Neha and Li, Jialiang and Aringunram, Ravi},
	year = {2018},
	keywords = {near real time data ingestion, olap, parallel and distributed dbmss, pinot},
	pages = {583--594}
}

@inproceedings{yan_carousel:_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Carousel: {Low}-{Latency} {Transaction} {Processing} for {Globally}-{Distributed} {Data}},
	isbn = {978-1-4503-4703-7},
	shorttitle = {Carousel},
	url = {http://doi.acm.org/10.1145/3183713.3196912},
	doi = {10.1145/3183713.3196912},
	abstract = {The trend towards global applications and services has created an increasing demand for transaction processing on globally-distributed data. Many database systems, such as Spanner and CockroachDB, support distributed transactions but require a large number of wide-area network roundtrips to commit each transaction and ensure the transaction's state is durably replicated across multiple datacenters. This can significantly increase transaction completion time, resulting in developers replacing database-level transactions with their own error-prone application-level solutions. This paper introduces Carousel, a distributed database system that provides low-latency transaction processing for multi-partition globally-distributed transactions. Carousel shortens transaction processing time by reducing the number of sequential wide-area network round trips required to commit a transaction and replicate its results while maintaining serializability. This is possible in part by using information about a transaction's potential write set to enable transaction processing, including any necessary remote read operations, to overlap with 2PC and state replication. Carousel further reduces transaction completion time by introducing a consensus protocol that can perform state replication in parallel with 2PC. For a multi-partition 2-round Fixed-set Interactive (2FI) transaction, Carousel requires at most two wide-area network roundtrips to commit the transaction when there are no failures, and only one round trip in the common case if local replicas are available.},
	urldate = {2018-06-04},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Yan, Xinan and Yang, Linguan and Zhang, Hongbo and Lin, Xiayue Charles and Wong, Bernard and Salem, Kenneth and Brecht, Tim},
	year = {2018},
	keywords = {distributed transactions, globally-distributed data},
	pages = {231--243}
}

@inproceedings{begoli_apache_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Apache {Calcite}: {A} {Foundational} {Framework} for {Optimized} {Query} {Processing} {Over} {Heterogeneous} {Data} {Sources}},
	isbn = {978-1-4503-4703-7},
	shorttitle = {Apache {Calcite}},
	url = {http://doi.acm.org/10.1145/3183713.3190662},
	doi = {10.1145/3183713.3190662},
	abstract = {Apache Calcite is a foundational software framework that provides query processing, optimization, and query language support to many popular open-source data processing systems such as Apache Hive, Apache Storm, Apache Flink, Druid, and MapD. The goal of this paper is to formally introduce Calcite to the broader research community, brie y present its history, and describe its architecture, features, functionality, and patterns for adoption. Calcite's architecture consists of a modular and extensible query optimizer with hundreds of built-in optimization rules, a query processor capable of processing a variety of query languages, an adapter architecture designed for extensibility, and support for heterogeneous data models and stores (relational, semi-structured, streaming, and geospatial). This exible, embeddable, and extensible architecture is what makes Calcite an attractive choice for adoption in big-data frameworks. It is an active project that continues to introduce support for the new types of data sources, query languages, and approaches to query processing and optimization.},
	urldate = {2018-06-04},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Begoli, Edmon and Camacho-Rodríguez, Jesús and Hyde, Julian and Mior, Michael J. and Lemire, Daniel},
	year = {2018},
	keywords = {apache calcite, data management, modular query optimization, query algebra, relational semantics, storage adapters},
	pages = {221--230}
}

@inproceedings{dai_fine-grained_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Fine-grained {Concept} {Linking} {Using} {Neural} {Networks} in {Healthcare}},
	isbn = {978-1-4503-4703-7},
	url = {http://doi.acm.org/10.1145/3183713.3196907},
	doi = {10.1145/3183713.3196907},
	abstract = {To unlock the wealth of the healthcare data, we often need to link the real-world text snippets to the referred medical concepts described by the canonical descriptions. However, existing healthcare concept linking methods, such as dictionary-based and simple machine learning methods, are not effective due to the word discrepancy between the text snippet and the canonical concept description, and the overlapping concept meaning among the fine-grained concepts. To address these challenges, we propose a Neural Concept Linking (NCL) approach for accurate concept linking using systematically integrated neural networks. We call the novel neural network architecture as the COMposite AttentIonal encode-Decode neural network (COM-AID). COM-AID performs an encode-decode process that encodes a concept into a vector and decodes the vector into a text snippet with the help of two devised contexts. On the one hand, it injects the textual context into the neural network through the attention mechanism, so that the word discrepancy can be overcome from the semantic perspective. On the other hand, it incorporates the structural context into the neural network through the attention mechanism, so that minor concept meaning differences can be enlarged and effectively differentiated. Empirical studies on two real-world datasets confirm that the NCL produces accurate concept linking results and significantly outperforms state-of-the-art techniques.},
	urldate = {2018-06-04},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Dai, Jian and Zhang, Meihui and Chen, Gang and Fan, Ju and Ngiam, Kee Yuan and Ooi, Beng Chin},
	year = {2018},
	keywords = {attention mechanism, fine-grained concept linking, healthcare, neural networks},
	pages = {51--66}
}

@inproceedings{yan_synthesizing_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Synthesizing {Type}-{Detection} {Logic} for {Rich} {Semantic} {Data} {Types} {Using} {Open}-source {Code}},
	isbn = {978-1-4503-4703-7},
	url = {http://doi.acm.org/10.1145/3183713.3196888},
	doi = {10.1145/3183713.3196888},
	abstract = {Given a table of data, existing systems can often detect basic atomic types (e.g., strings vs. numbers) for each column. A new generation of data-analytics and data-preparation systems are starting to automatically recognize rich semantic types such as date-time, email address, etc., for such metadata can bring an array of benefits including better table understanding, improved search relevance, precise data validation, and semantic data transformation. However, existing approaches only detect a limited number of types using regular-expression-like patterns, which are often inaccurate, and cannot handle rich semantic types such as credit card and ISBN numbers that encode semantic validations (e.g., checksum). We developed AUTOTYPE from open-source repositories like GitHub. Users only need to provide a set of positive examples for a target data type and a search keyword, our system will automatically identify relevant code, and synthesize type-detection functions using execution traces. We compiled a benchmark with 112 semantic types, out of which the proposed system can synthesize code to detect 84 such types at a high precision. Applying the synthesized type-detection logic on web table columns have also resulted in a significant increase in data types discovered compared to alternative approaches.},
	urldate = {2018-06-04},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Yan, Cong and He, Yeye},
	year = {2018},
	keywords = {code search, data preparation, metadata management, open-source code, semantic data types, type detection},
	pages = {35--50}
}

@inproceedings{mudgal_deep_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Deep {Learning} for {Entity} {Matching}: {A} {Design} {Space} {Exploration}},
	isbn = {978-1-4503-4703-7},
	shorttitle = {Deep {Learning} for {Entity} {Matching}},
	url = {http://doi.acm.org/10.1145/3183713.3196926},
	doi = {10.1145/3183713.3196926},
	abstract = {Entity matching (EM) finds data instances that refer to the same real-world entity. In this paper we examine applying deep learning (DL) to EM, to understand DL's benefits and limitations. We review many DL solutions that have been developed for related matching tasks in text processing (e.g., entity linking, textual entailment, etc.). We categorize these solutions and define a space of DL solutions for EM, as embodied by four solutions with varying representational power: SIF, RNN, Attention, and Hybrid. Next, we investigate the types of EM problems for which DL can be helpful. We consider three such problem types, which match structured data instances, textual instances, and dirty instances, respectively. We empirically compare the above four DL solutions with Magellan, a state-of-the-art learning-based EM solution. The results show that DL does not outperform current solutions on structured EM, but it can significantly outperform them on textual and dirty EM. For practitioners, this suggests that they should seriously consider using DL for textual and dirty EM problems. Finally, we analyze DL's performance and discuss future research directions.},
	urldate = {2018-06-04},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Mudgal, Sidharth and Li, Han and Rekatsinas, Theodoros and Doan, AnHai and Park, Youngchoon and Krishnan, Ganesh and Deep, Rohit and Arcaute, Esteban and Raghavendra, Vijay},
	year = {2018},
	keywords = {deep learning, entity matching, entity resolution},
	pages = {19--34}
}

@inproceedings{galhotra_robust_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Robust {Entity} {Resolution} {Using} {Random} {Graphs}},
	isbn = {978-1-4503-4703-7},
	url = {http://doi.acm.org/10.1145/3183713.3183755},
	doi = {10.1145/3183713.3183755},
	abstract = {Entity resolution (ER) seeks to identify which records in a data set refer to the same real-world entity. Given the diversity of ways in which entities can be represented, matched and distinguished, ER is known to be a challenging task for automated strategies, but relatively easier for expert humans. In our work, we abstract the knowledge of experts with the notion of a binary oracle. Our oracle can answer questions of the form "do records u and v refer to the same entity?" under a flexible error model, allowing for some questions to be more difficult to answer correctly than others. Our contribution is a general error correction tool that can be leveraged by a variety of hybrid-human machine ER algorithms, based on a formal way for selecting indirect "control queries''. In our experiments we demonstrate that correction-less ER algorithms equipped with our tool can perform even better than recent ER algorithms specifically designed for correcting errors. Our control queries are selected among those that provide strongest connectivity between records of each cluster, based on the concept ofgraph expanders (which are sparse graphs with formal connectivity properties). We give formal performance guarantees for our toolkit and provide experiments on real and synthetic data.},
	urldate = {2018-06-04},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Galhotra, Sainyam and Firmani, Donatella and Saha, Barna and Srivastava, Divesh},
	year = {2018},
	keywords = {crowdsourcing, data cleaning, entity resolution, expander graphs},
	pages = {3--18}
}

@inproceedings{wu_data_2018,
	address = {New York, NY, USA},
	series = {{SIGMOD} '18},
	title = {Data {Citation}: {Giving} {Credit} {Where} {Credit} is {Due}},
	isbn = {978-1-4503-4703-7},
	shorttitle = {Data {Citation}},
	url = {http://doi.acm.org/10.1145/3183713.3196910},
	doi = {10.1145/3183713.3196910},
	abstract = {An increasing amount of information is being published in structured databases and retrieved using queries, raising the question of how query results should be cited. Since there are a large number of possible queries over a database, one strategy is to specify citations to a small set of frequent queries - citation views - and use these to construct citations to other "general" queries. We present three approaches to implementing citation views and describe alternative policies for the joint, alternate and aggregated use of citation views. Extensive experiments using both synthetic and realistic citation views and queries show the trade-offs between the approaches in terms of the time to generate citations, as well as the size of the resulting citation. They also show that the choice of policy has a huge effect both on performance and size, leading to useful guidelines for what policies to use and how to specify citation views.},
	urldate = {2018-06-04},
	booktitle = {Proceedings of the 2018 {International} {Conference} on {Management} of {Data}},
	publisher = {ACM},
	author = {Wu, Yinjun and Alawini, Abdussalam and Davidson, Susan B. and Silvello, Gianmaria},
	year = {2018},
	keywords = {data citation, provenance, scientific databases},
	pages = {99--114}
}

@inproceedings{hashavit_effective_2016,
	address = {New York, NY, USA},
	series = {{SIGIR} '16},
	title = {Effective {Trend} {Detection} {Within} a {Dynamic} {Search} {Context}},
	isbn = {978-1-4503-4069-4},
	url = {http://doi.acm.org/10.1145/2911451.2914705},
	doi = {10.1145/2911451.2914705},
	abstract = {In recent years, studies about trend detection in online social media streams have begun to emerge. Since not all users are likely to always be interested in the same set of trends, some of the research also focused on personalizing the trends by using some predefined personalized context. In this paper, we take this problem further to a setting in which the user's context is not predefined, but rather determined as the user issues a query. This presents a new challenge since trends cannot be computed ahead of time using high latency algorithms. We present RT-Trend, an online trend detection algorithm that promptly finds relevant in-context trends as users issue search queries over a dataset of documents. We evaluate our approach using real data from an online social network by assessing its ability to predict actual activity increase of social network entities in the context of a search result. Since we implemented this feature into an existing tool with an active pool of users, we also report click data, which suggests positive feedback.},
	urldate = {2018-05-15},
	booktitle = {Proceedings of the 39th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {ACM},
	author = {Hashavit, Anat and Levin, Roy and Guy, Ido and Kutiel, Gilad},
	year = {2016},
	keywords = {analytics, tag cloud, trend cloud, trends, word cloud},
	pages = {817--820}
}

@misc{noauthor_doc2sent2vec_nodate,
	title = {Doc2Sent2Vec},
	url = {https://dl.acm.org/citation.cfm?id=2914717},
	urldate = {2018-05-15}
}

@inproceedings{moreo_distributional_2016,
	address = {New York, NY, USA},
	series = {{SIGIR} '16},
	title = {Distributional {Random} {Oversampling} for {Imbalanced} {Text} {Classification}},
	isbn = {978-1-4503-4069-4},
	url = {http://doi.acm.org/10.1145/2911451.2914722},
	doi = {10.1145/2911451.2914722},
	abstract = {The accuracy of many classification algorithms is known to suffer when the data are imbalanced (i.e., when the distribution of the examples across the classes is severely skewed). Many applications of binary text classification are of this type, with the positive examples of the class of interest far outnumbered by the negative examples. Oversampling (i.e., generating synthetic training examples of the minority class) is an often used strategy to counter this problem. We present a new oversampling method specifically designed for classifying data (such as text) for which the distributional hypothesis holds, according to which the meaning of a feature is somehow determined by its distribution in large corpora of data. Our Distributional Random Oversampling method generates new random minority-class synthetic documents by exploiting the distributional properties of the terms in the collection. We discuss results we have obtained on the Reuters-21578, OHSUMED-S, and RCV1-v2 datasets.},
	urldate = {2018-05-15},
	booktitle = {Proceedings of the 39th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {ACM},
	author = {Moreo, Alejandro and Esuli, Andrea and Sebastiani, Fabrizio},
	year = {2016},
	keywords = {distributional hypothesis, imbalanced text classification, oversampling},
	pages = {805--808}
}

@inproceedings{yu_dynamic_2016,
	address = {New York, NY, USA},
	series = {{SIGIR} '16},
	title = {A {Dynamic} {Recurrent} {Model} for {Next} {Basket} {Recommendation}},
	isbn = {978-1-4503-4069-4},
	url = {http://doi.acm.org/10.1145/2911451.2914683},
	doi = {10.1145/2911451.2914683},
	abstract = {Next basket recommendation becomes an increasing concern. Most conventional models explore either sequential transaction features or general interests of users. Further, some works treat users' general interests and sequential behaviors as two totally divided matters, and then combine them in some way for next basket recommendation. Moreover, the state-of-the-art models are based on the assumption of Markov Chains (MC), which only capture local sequential features between two adjacent baskets. In this work, we propose a novel model, Dynamic REcurrent bAsket Model (DREAM), based on Recurrent Neural Network (RNN). DREAM not only learns a dynamic representation of a user but also captures global sequential features among baskets. The dynamic representation of a specific user can reveal user's dynamic interests at different time, and the global sequential features reflect interactions of all baskets of the user over time. Experiment results on two public datasets indicate that DREAM is more effective than the state-of-the-art models for next basket recommendation.},
	urldate = {2018-05-15},
	booktitle = {Proceedings of the 39th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {ACM},
	author = {Yu, Feng and Liu, Qiang and Wu, Shu and Wang, Liang and Tan, Tieniu},
	year = {2016},
	keywords = {next basket recommendation, recurrent neural network},
	pages = {729--732}
}

@inproceedings{zhao_explainable_2016,
	address = {New York, NY, USA},
	series = {{SIGIR} '16},
	title = {Explainable {User} {Clustering} in {Short} {Text} {Streams}},
	isbn = {978-1-4503-4069-4},
	url = {http://doi.acm.org/10.1145/2911451.2911522},
	doi = {10.1145/2911451.2911522},
	abstract = {User clustering has been studied from different angles: behavior-based, to identify similar browsing or search patterns, and content-based, to identify shared interests. Once user clusters have been found, they can be used for recommendation and personalization. So far, content-based user clustering has mostly focused on static sets of relatively long documents. Given the dynamic nature of social media, there is a need to dynamically cluster users in the context of short text streams. User clustering in this setting is more challenging than in the case of long documents as it is difficult to capture the users' dynamic topic distributions in sparse data settings. To address this problem, we propose a dynamic user clustering topic model (or UCT for short). UCT adaptively tracks changes of each user's time-varying topic distribution based both on the short texts the user posts during a given time period and on the previously estimated distribution. To infer changes, we propose a Gibbs sampling algorithm where a set of word-pairs from each user is constructed for sampling. The clustering results are explainable and human-understandable, in contrast to many other clustering algorithms. For evaluation purposes, we work with a dataset consisting of users and tweets from each user. Experimental results demonstrate the effectiveness of our proposed clustering model compared to state-of-the-art baselines.},
	urldate = {2018-05-15},
	booktitle = {Proceedings of the 39th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {ACM},
	author = {Zhao, Yukun and Liang, Shangsong and Ren, Zhaochun and Ma, Jun and Yilmaz, Emine and de Rijke, Maarten},
	year = {2016},
	keywords = {short text processing, user clustering, user topic modeling},
	pages = {155--164}
}

@inproceedings{li_topic_2016,
	address = {New York, NY, USA},
	series = {{SIGIR} '16},
	title = {Topic {Modeling} for {Short} {Texts} with {Auxiliary} {Word} {Embeddings}},
	isbn = {978-1-4503-4069-4},
	url = {http://doi.acm.org/10.1145/2911451.2911499},
	doi = {10.1145/2911451.2911499},
	abstract = {For many applications that require semantic understanding of short texts, inferring discriminative and coherent latent topics from short texts is a critical and fundamental task. Conventional topic models largely rely on word co-occurrences to derive topics from a collection of documents. However, due to the length of each document, short texts are much more sparse in terms of word co-occurrences. Data sparsity therefore becomes a bottleneck for conventional topic models to achieve good results on short texts. On the other hand, when a human being interprets a piece of short text, the understanding is not solely based on its content words, but also her background knowledge (e.g., semantically related words). The recent advances in word embedding offer effective learning of word semantic relations from a large corpus. Exploiting such auxiliary word embeddings to enrich topic modeling for short texts is the main focus of this paper. To this end, we propose a simple, fast, and effective topic model for short texts, named GPU-DMM. Based on the Dirichlet Multinomial Mixture (DMM) model, GPU-DMM promotes the semantically related words under the same topic during the sampling process by using the generalized Polya urn (GPU) model. In this sense, the background knowledge about word semantic relatedness learned from millions of external documents can be easily exploited to improve topic modeling for short texts. Through extensive experiments on two real-world short text collections in two languages, we show that GPU-DMM achieves comparable or better topic representations than state-of-the-art models, measured by topic coherence. The learned topic representation leads to the best accuracy in text classification task, which is used as an indirect evaluation.},
	urldate = {2018-05-15},
	booktitle = {Proceedings of the 39th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {ACM},
	author = {Li, Chenliang and Wang, Haoran and Zhang, Zhiqian and Sun, Aixin and Ma, Zongyang},
	year = {2016},
	keywords = {short texts, topic model, word embeddings},
	pages = {165--174}
}

@inproceedings{borisov_context-aware_2016,
	address = {New York, NY, USA},
	series = {{SIGIR} '16},
	title = {A {Context}-aware {Time} {Model} for {Web} {Search}},
	isbn = {978-1-4503-4069-4},
	url = {http://doi.acm.org/10.1145/2911451.2911504},
	doi = {10.1145/2911451.2911504},
	abstract = {In web search, information about times between user actions has been shown to be a good indicator of users' satisfaction with the search results. Existing work uses the mean values of the observed times, or fits probability distributions to the observed times. This implies a context-independence assumption that the time elapsed between a pair of user actions does not depend on the context, in which the first action takes place. We validate this assumption using logs of a commercial web search engine and discover that it does not always hold. For between 37\% to 80\% of query-result pairs, depending on the number of observations, the distributions of click dwell times have statistically significant differences in query sessions for which a given result (i) is the first item to be clicked and (ii) is not the first. To account for this context bias effect, we propose a context-aware time model (CATM). The CATM allows us (i) to predict times between user actions in contexts, in which these actions were not observed, and (ii) to compute context-independent estimates of the times by predicting them in predefined contexts. Our experimental results show that the CATM provides better means than existing methods to predict and interpret times between user actions.},
	urldate = {2018-05-15},
	booktitle = {Proceedings of the 39th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {ACM},
	author = {Borisov, Alexey and Markov, Ilya and de Rijke, Maarten and Serdyukov, Pavel},
	year = {2016},
	keywords = {time modeling, user behavior, web search},
	pages = {205--214}
}

@inproceedings{gog_fast_2016,
	address = {New York, NY, USA},
	series = {{SIGIR} '16},
	title = {Fast and {Compact} {Hamming} {Distance} {Index}},
	isbn = {978-1-4503-4069-4},
	url = {http://doi.acm.org/10.1145/2911451.2911523},
	doi = {10.1145/2911451.2911523},
	abstract = {Searching for similar objects in a collection is a core task of many applications in databases, pattern recognition, and information retrieval. As there exist similarity-preserving hash functions like SimHash, indexing these objects reduces to the solution of the Approximate Dictionary Queries problem. In this problem we have to index a collection of fixed-sized keys to efficiently retrieve all the keys which are at a Hamming distance at most k from a query key. In this paper we propose new solutions for the approximate dictionary queries problem. These solutions combine the use of succinct data structures with an efficient representation of the keys to significantly reduce the space usage of the state-of-the-art solutions without introducing any time penalty. Finally, by exploiting triangle inequality, we can also significantly speed up the query time of the existing solutions.},
	urldate = {2018-05-15},
	booktitle = {Proceedings of the 39th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {ACM},
	author = {Gog, Simon and Venturini, Rossano},
	year = {2016},
	keywords = {hamming distance, indexing},
	pages = {285--294}
}

@inproceedings{moshfeghi_understanding_2016,
	address = {New York, NY, USA},
	series = {{SIGIR} '16},
	title = {Understanding {Information} {Need}: {An} {fMRI} {Study}},
	isbn = {978-1-4503-4069-4},
	shorttitle = {Understanding {Information} {Need}},
	url = {http://doi.acm.org/10.1145/2911451.2911534},
	doi = {10.1145/2911451.2911534},
	abstract = {The raison d'etre of IR is to satisfy human information need. But, do we really understand information need? Despite advances in the past few decades in both the IR and relevant scientific communities, this question is largely unanswered. We do not really understand how an information need emerges and how it is physically manifested. Information need refers to a complex concept: at the very initial state of the phenomenon (i.e. at a visceral level), even the searcher may not be aware of its existence. This renders the measuring of this concept (using traditional behaviour studies) nearly impossible. In this paper, we investigate the connection between an information need and brain activity. Using functional Magnetic Resonance Imaging (fMRI), we measured the brain activity of twenty four participants while they performed a Question Answering (Q/A) Task, where the questions were carefully selected and developed from TREC-8 and TREC 2001 Q/A Track. The results of this experiment revealed a distributed network of brain regions commonly associated with activities related to information need and retrieval and differing brain activity in processing scenarios when participants knew the answer to a given question and when they did not and needed to search. We believe our study and conclusions constitute an important step in unravelling the nature of information need and therefore better satisfying it.},
	urldate = {2018-05-15},
	booktitle = {Proceedings of the 39th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {ACM},
	author = {Moshfeghi, Yashar and Triantafillou, Peter and Pollick, Frank E.},
	year = {2016},
	keywords = {anomalous states of knowledge, fmri study, information need, information retrieval},
	pages = {335--344}
}

@inproceedings{alonso_data_2016,
	address = {New York, NY, USA},
	series = {{SIGIR} '16},
	title = {The {Data} {Stack} in {Information} {Retrieval}},
	isbn = {978-1-4503-4069-4},
	url = {http://doi.acm.org/10.1145/2911451.2926726},
	doi = {10.1145/2911451.2926726},
	abstract = {I propose to look at information retrieval applications from the perspective of the data stack infrastructure that is needed in research prototypes and production systems.},
	urldate = {2018-05-15},
	booktitle = {Proceedings of the 39th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {ACM},
	author = {Alonso, Omar},
	year = {2016},
	keywords = {applications., big data, data science, infrastructure, small data},
	pages = {597--597}
}

@inproceedings{braem_designing_2016,
	title = {Designing a smart city playground: {Real}-time air quality measurements and visualization in the {City} of {Things} testbed},
	shorttitle = {Designing a smart city playground},
	doi = {10.1109/ISC2.2016.7580871},
	abstract = {While smart cities have the potential to monitor and control the city in real-time through sensors and actuators, there is still an important road ahead to evolve from isolated smart city experiments to real large-scale deployments. The City of Things testbed, which is a smart city testbed located in the city of Antwerp, Belgium wants to address the underlying research challenges by means of a multi-technology network infrastructure, the capacity to easily perform data experiments on top and a living lab approach to validate the experiments. The demo described in this paper showcases a first use case scenario, visualizing live air quality data that is monitored via sensors installed on top of mobile post vans, driving around in the City of Antwerp. By means of this test demo we will further detail how our generic test bed enables the dynamic integration and support of multiple wireless technologies, novel data processing use cases scenarios as well as the active user panel involvement.},
	booktitle = {2016 {IEEE} {International} {Smart} {Cities} {Conference} ({ISC}2)},
	author = {Braem, B. and Latre, S. and Leroux, P. and Demeester, P. and Coenen, T. and Ballon, P.},
	month = sep,
	year = {2016},
	keywords = {Air quality, Belgium, Internet of Things, Logic gates, Monitoring, Real-time systems, Sensors, Smart cities, actuators, air quality, city of Antwerp, city of things testbed, data processing, data visualisation, dynamic integration, environmental science computing, live air quality data visualization, multiple wireless technologies, multitechnology network infrastructure, real-time air quality measurements, real-time air quality visualization, sensors, smart cities, smart city playground},
	pages = {1--2}
}

@inproceedings{latre_city_2016,
	title = {City of things: {An} integrated and multi-technology testbed for {IoT} smart city experiments},
	shorttitle = {City of things},
	doi = {10.1109/ISC2.2016.7580875},
	abstract = {While smart cities have the potential to monitor and control the city in real-time through sensors and actuators, there is still an important road ahead to evolve from isolated smart city experiments to real large-scale deployments. Important research questions remain on how and which wireless technologies should be setup for connecting the city, how the data should be analysed and how the acceptance by users of applications can be assessed. In this paper we present the City of Things testbed, which is a smart city testbed located in the city of Antwerp, Belgium to address these questions. It allows the setup and validation of new smart city experiments both at a technology and user level. City of Things consists of a multi-wireless technology network infrastructure, the capacity to easily perform data experiments on top and a living lab approach to validate the experiments. In comparison to other smart city testbeds, City of Things consists of an integrated approach, allowing experimentation on three different layers: networks, data and living lab while supporting a wide range of wireless technologies. We give an overview of the City of Things architecture, explain how researchers can perform smart city experiments and illustrate this by a case study on air quality.},
	booktitle = {2016 {IEEE} {International} {Smart} {Cities} {Conference} ({ISC}2)},
	author = {Latre, S. and Leroux, P. and Coenen, T. and Braem, B. and Ballon, P. and Demeester, P.},
	month = sep,
	year = {2016},
	keywords = {Antwerp, Belgium, Intelligent sensors, Internet of Things, IoT smart city, Logic gates, Smart cities, Wireless communication, Wireless sensor networks, actuators, city of things, multitechnology testbed, multiwireless technology network, sensors, smart cities, wireless technologies},
	pages = {1--8}
}

@inproceedings{vanhove_tengu:_2015,
	title = {Tengu: {An} {Experimentation} {Platform} for {Big} {Data} {Applications}},
	shorttitle = {Tengu},
	doi = {10.1109/ICDCSW.2015.19},
	abstract = {Big data applications have stringent service requirements for scalability and fault-tolerance and involve high volumes of data, high processing speeds and large varieties of database technologies. In order to test big data management solutions, large experimentation facilities are needed, which are expensive in terms of both resource cost and configuration time. This paper presents Tengu, an experimentation platform for big data applications that can automatically be instantiated on GENI (US federation of test beds) and Fed FIRE (EU federation of test beds)compatible test beds. Tengu allows for automatic deployments of several data processing, storage and cloud technologies, including Hadoop, Storm and Open Stack. The paper discusses the Tengu architecture, the Tengu-as-a-service approach and a demonstration of an automated instantiation of the Tengu experimentation suite on the Virtual Wall, a large-scale Emulab testbed at the Minds research institute in Europe.},
	booktitle = {2015 {IEEE} 35th {International} {Conference} on {Distributed} {Computing} {Systems} {Workshops}},
	author = {Vanhove, T. and Seghbroeck, G. V. and Wauters, T. and Turck, F. D. and Vermeulen, B. and Demeester, P.},
	month = jun,
	year = {2015},
	keywords = {Big Data, Big data, Computer architecture, Distributed databases, EU federation of test beds, Fed4FIRE compatible test beds, GENI compatible test beds, Hadoop, Monitoring, OpenStack, Servers, Software, Storm, Storms, Tengu-as-a-service approach, US federation of test beds, big data management solutions, cloud computing, cloud technologies, configuration time, data processing, database technologies, fault-tolerance, iMinds research institute, large-scale Emulab testbed, resource cost, storage technologies, virtual wall},
	pages = {42--47}
}

@inproceedings{luu_secure_2016,
	address = {New York, NY, USA},
	series = {{CCS} '16},
	title = {A {Secure} {Sharding} {Protocol} {For} {Open} {Blockchains}},
	isbn = {978-1-4503-4139-4},
	url = {http://doi.acm.org/10.1145/2976749.2978389},
	doi = {10.1145/2976749.2978389},
	abstract = {Cryptocurrencies, such as Bitcoin and 250 similar alt-coins, embody at their core a blockchain protocol --- a mechanism for a distributed network of computational nodes to periodically agree on a set of new transactions. Designing a secure blockchain protocol relies on an open challenge in security, that of designing a highly-scalable agreement protocol open to manipulation by byzantine or arbitrarily malicious nodes. Bitcoin's blockchain agreement protocol exhibits security, but does not scale: it processes 3--7 transactions per second at present, irrespective of the available computation capacity at hand. In this paper, we propose a new distributed agreement protocol for permission-less blockchains called ELASTICO. ELASTICO scales transaction rates almost linearly with available computation for mining: the more the computation power in the network, the higher the number of transaction blocks selected per unit time. ELASTICO is efficient in its network messages and tolerates byzantine adversaries of up to one-fourth of the total computational power. Technically, ELASTICO uniformly partitions or parallelizes the mining network (securely) into smaller committees, each of which processes a disjoint set of transactions (or "shards"). While sharding is common in non-byzantine settings, ELASTICO is the first candidate for a secure sharding protocol with presence of byzantine adversaries. Our scalability experiments on Amazon EC2 with up to \$1, 600\$ nodes confirm ELASTICO's theoretical scaling properties.},
	urldate = {2018-04-29},
	booktitle = {Proceedings of the 2016 {ACM} {SIGSAC} {Conference} on {Computer} and {Communications} {Security}},
	publisher = {ACM},
	author = {Luu, Loi and Narayanan, Viswesh and Zheng, Chaodong and Baweja, Kunal and Gilbert, Seth and Saxena, Prateek},
	year = {2016},
	keywords = {bitcoin, consensus protocol, sharding},
	pages = {17--30}
}

@inproceedings{mattera_practical_2017,
	title = {A {Practical} {Approach} to {Validation} of {Buildings} \#x2019; {Sensor} {Data}: {A} {Commissioning} {Experience} {Report}},
	shorttitle = {A {Practical} {Approach} to {Validation} of {Buildings} \#x2019; {Sensor} {Data}},
	doi = {10.1109/BigDataService.2017.48},
	abstract = {Often manually performed commissioning processes on building's sensors fail to systematically validate that all building's sensors operate correctly. This is so because manual processes are tedious and only inspect a limited number of sensors. As a result, sensors are often uncalibrated, biased or somehow faulty, impacting building's behaviour, comfort level and energy usage. We present a practical approach to automatically validate data from all building's sensors. We designed and implemented four different tests to detect out-of-range values, spikes, latency issues and non-monotonous values. Our tests are based on expert knowledge and do not need historical data. We ran the validation tests on a newly constructed building at the campus of the University of Southern Denmark. As a result we identified two types of faulty behaviours in the building's sensors: CO2 sensors reporting biased values and temperature sensors' readings exhibiting high latency. We show how automatic data validation for building sensors enhances the processes of detecting issues which could severely impact building's operations, and were otherwise going unnoticed. Thus, we emphasize the importance of performing data validation as a necessity for a correct building operation.},
	booktitle = {2017 {IEEE} {Third} {International} {Conference} on {Big} {Data} {Computing} {Service} and {Applications} ({BigDataService})},
	author = {Mattera, C. G. and Lazarova-Molnar, S. and Shaker, H. R. and Jørgensen, B. N.},
	month = apr,
	year = {2017},
	keywords = {BMS, Buildings, Data models, Heating systems, Temperature distribution, Temperature measurement, Temperature sensors, University of Southern Denmark, Ventilation, building behaviour, building management systems, building operations, buildings sensor data validation, comfort level, data handling, energy usage, expert knowledge, fault detection and diagnosis, manual processes, practical approach, sensor data validation, sensors, smart buildings, temperature sensors},
	pages = {287--292}
}

@inproceedings{zhou_advanced_2017,
	title = {An {Advanced} {Inventory} {Data} {Mining} {System} for {Business} {Intelligence}},
	doi = {10.1109/BigDataService.2017.36},
	abstract = {Inventory management plays a critical role to track inventory levels, orders, and sales of the retailing business. Effective inventory management is a capability necessary to lead in the global marketplace. In the current retailing market, a huge amount of data regarding stocked items in inventory is generated and collected every day. Due to the increasing volume of transaction data and their correlated relations, it is often a non-trivial task to efficiently manage stocked goods, yet it is imperative to explore the underlying dependencies of the inventory items and give insights into implementing intelligent management systems. However, existing inventory management systems rely on statistical analysis of the historical inventory data, and have a limited capability of intelligent management. For example, they usually do not have the ability to forecast item demand and detect anomalous patterns of item inventory transactions. There is little work reported in implementing intelligent inventory management solutions to reveal hidden relations with integrated data-driven analysis. In this paper, we present an intelligent system, called iMiner, to facilitate managing enormous inventory data. We utilize distributed computing resources to process the huge volume of inventory data and incorporate the latest advances in data mining technologies. iMiner provides comprehensive support for conducting many inventory management tasks, such as forecasting inventory, detecting anomalous items, and analyzing inventory aging.},
	booktitle = {2017 {IEEE} {Third} {International} {Conference} on {Big} {Data} {Computing} {Service} and {Applications} ({BigDataService})},
	author = {Zhou, Q. and Xia, B. and Xue, W. and Zeng, C. and Han, R. and Li, T.},
	month = apr,
	year = {2017},
	keywords = {Data analysis, Data mining, Forecasting, Inventory management, Predictive models, Time series analysis, advanced inventory data mining system, business intelligence, competitive intelligence, data mining, distributed computing resources, distributed processing, global marketplace, historical inventory data, iMiner, integrated data-driven analysis, intelligent management systems, inventory levels, inventory management, orders, production engineering computing, retailing, retailing business, retailing market, statistical analysis, stocked goods, stocked items, transaction data},
	pages = {210--217}
}

@inproceedings{gao_rapidly_2017,
	title = {Rapidly {Generate} and {Visualize} the {Digest} of {Massive} {Time} {Series} {Data}},
	doi = {10.1109/BigDataService.2017.25},
	abstract = {Time series data are a set of data high-frequently collected in chronological order and the huge amount causes difficulty in observation and utilization. Thus, we present a method to rapidly generate and visualize the digest of massive time series data. The method comprises the following steps: linearly scanning the input time series data, dividing the time series data into a plurality of data intervals, calculating the statistical data, characterizing the data features in each interval, and adopting an improved candlestick chart to show the trend and distribution of the data series. This method is less complex with small amount of data, and can store and query data in relational database. The improved visualization method of candlestick charts is more practical and operable, which can visualize the distribution of data through color change showed in the candlestick charts. During the visualizing process, it can support the merging and separating of the intervals at different levels and granularity. Through experiments, it is proved that this method is efficient and useful.},
	booktitle = {2017 {IEEE} {Third} {International} {Conference} on {Big} {Data} {Computing} {Service} and {Applications} ({BigDataService})},
	author = {Gao, Z. and Zou, T. and Zhang, L. and Chen, J. and Guo, J. and Ji, W.},
	month = apr,
	year = {2017},
	keywords = {Candlestick charts, Data digest, Data preprocessing, Data visualization, Merging, Radiation detectors, Relational databases, Standards, Thermodynamic diagram, Time series analysis, Time series data, candlestick charts, chronological order, data intervals, data visualisation, input time series data, linearly scanning, massive time series data, query processing, relational database, relational databases, statistical data, time series, visualizing process},
	pages = {157--164}
}

@inproceedings{fu_balanced_2017,
	title = {Balanced {Parallel} {Frequent} {Pattern} {Mining} over {Massive} {Data} {Stream}},
	doi = {10.1109/BigDataService.2017.15},
	abstract = {Frequent pattern mining is playing an increasingly important role in a growing number of real-time data flow scenarios, such as large-scale order stream data, network traffic monitoring, web accessing record stream, and so on. The continuous, unbounded and high speed characteristics of massive data stream are a huge challenge for the current frequent pattern mining approach. The main challenge is that, as data stream continuously arriving, the non frequent patterns discarded can possibly become frequent again. In this paper, aimed at the characteristics of real-time data stream, we propose a compact data structure, called CPS-tree to maintain and operate the full information of data stream. Compared to current related works, our algorithm can dynamically support large-scale data stream with one-pass scan which can be easily applied to other data stream processing environments; Moreover, the load imbalance in the current frequent pattern mining is a pretty common problem. We analysis the features of data stream, and propose a depth-based strategy to solve the imbalance problem in our parallel algorithm. In conclusion, we propose the BPFPMS algorithm, a balanced parallel frequent pattern mining over massive data stream, to dynamically and efficiently mine frequent patterns over large scale data stream. Our experiments show that our algorithm can achieve a good speedup and a good degree of balance among each node with different degree of parallelism.},
	booktitle = {2017 {IEEE} {Third} {International} {Conference} on {Big} {Data} {Computing} {Service} and {Applications} ({BigDataService})},
	author = {Fu, X. and Shi, L. and Li, J.},
	month = apr,
	year = {2017},
	keywords = {Algorithm design and analysis, BPFPMS algorithm, Big Data, CPS-tree, Data mining, Data structures, Heuristic algorithms, Itemsets, Machine learning algorithms, balanced parallel frequent pattern mining, compact data structure, data mining, data stream processing environments, depth-based strategy, large-scale data stream, load imbalance, massive data stream, one-pass scan, parallel algorithm, parallel algorithms, real-time data flow scenarios, real-time data stream, tree data structures},
	pages = {50--59}
}

@inproceedings{park_goal-oriented_2017,
	title = {A {Goal}-{Oriented} {Big} {Data} {Analytics} {Framework} for {Aligning} with {Business}},
	doi = {10.1109/BigDataService.2017.29},
	abstract = {Big data analytics is the hottest new technology which helps turn hidden insights in big data into business value to support a better decision-making. However, current big data analytics has many challenges to do it since there is a big gap between big data analytics and business. This is mainly because lack of business context around the data, lack of expertise to connect the dots, and implicit business objectives. In this paper, we present IRIS - a big data analytics framework for aligning with business in a goal-oriented approach. It is composed of ontology for a business context model, analytics methods for connecting big data with business, an action process for collaborative work and an assistant tool utilizing Spark. In this framework, problems of the current process and solutions for the future process are hypothesized in an explicit business context model and validated them by using diverse analytics methods implemented on top of Spark libraries. Also, a goal-oriented approach enables to explore and select alternatives among potential problems and solutions. A business process for clearance pricing decision is used to show how big data analytics can be turned into business value by using our framework which align big data to business goals, as well as for an initial understanding of the applicability of IRIS.},
	booktitle = {2017 {IEEE} {Third} {International} {Conference} on {Big} {Data} {Computing} {Service} and {Applications} ({BigDataService})},
	author = {Park, G. and Chung, L. and Zhao, L. and Supakkul, S.},
	month = apr,
	year = {2017},
	keywords = {Analytical models, Big Data, Big Data Analytics, Business, Business Alignment, Business Process, Context, Goal-Orientation, IRIS, Iris, Ontologies, Spark libraries, Sparks, action process, analytics methods, business alignment, business data processing, business value, clearance pricing decision, collaborative work, data analysis, decision making, decision-making, diverse analytics methods, explicit business context model, goal-oriented Big Data analytics framework, groupware, ontologies (artificial intelligence), ontology, pricing},
	pages = {31--40}
}

@inproceedings{patil_enriched_2017,
	title = {Enriched {Over}\_Sampling {Techniques} for {Improving} {Classification} of {Imbalanced} {Big} {Data}},
	doi = {10.1109/BigDataService.2017.19},
	abstract = {Big Data generated in exabytes per year has become a watchword of today's research. They are exceptionally afar from the capability of commonly used software tools and also beyond the handling possibility of the single machine architecture. Facing this challenge has activated a requisite to reexamine the data management options. The new avenues of NoSQL Big Data compared to the traditional forms has insisted on adapting experimental beds, helping to discover large unknown values from enormous data sets. Also, outmoded management systems and statistical packages express trouble handling Big Data. In numerous real applications, handling of imbalanced data sets is the fact of precedence. The classification of data sets having imbalanced class distribution has produced a notable drawback in performance obtained by the most standard classifier learning algorithms. Assuming balanced class distribution and equal misclassification costs lead to poor results. In a real-world domain, the classification methods of multi-class imbalance problem need more attention compared to the two-class problem. A methodology is presented for binary/multi-class imbalanced data sets with improved over\_sampling (O.S.) techniques to enhance classification. The methods are broadly classified into two categories: non-clustered and cluster based advanced approach compared to prior work on O.S. techniques. The balanced data are subsequently analyzed for classification using various classifiers. Proposed techniques are performed using mapreduce environment on Apache Hadoop, using various data sets from UCI/KEEL repository. Fmeasures and ROC area are used to measure the performance of this classification.},
	booktitle = {2017 {IEEE} {Third} {International} {Conference} on {Big} {Data} {Computing} {Service} and {Applications} ({BigDataService})},
	author = {Patil, S. S. and Sonavane, S. P.},
	month = apr,
	year = {2017},
	keywords = {Apache Hadoop, Area measurement, Big Data, Cancer, Classification algorithms, Data mining, F measures, Machine learning algorithms, MapReduce environment, NoSQL Big Data, ROC area, Standards, UCI/KEEL repository, balanced class distribution, binary/multiclass imbalanced data sets, classifiers, cluster based advanced approach, data level approach, data management options, enriched oversampling techniques, equal misclassification costs, imbalanced big data classification, imbalanced class distribution, imbalanced data set handling, imbalanced data sets, learning (artificial intelligence), multiclass imbalance problem, nonclustered based advanced approach, over\_sampling techniques, pattern classification, pattern clustering, standard classifier learning algorithms, two-class problem},
	pages = {1--10}
}

@article{mehta_comparative_2017,
	title = {Comparative {Evaluation} of {Big}-data {Systems} on {Scientific} {Image} {Analytics} {Workloads}},
	volume = {10},
	issn = {2150-8097},
	url = {https://doi.org/10.14778/3137628.3137634},
	doi = {10.14778/3137628.3137634},
	abstract = {Scientific discoveries are increasingly driven by analyzing large volumes of image data. Many new libraries and specialized database management systems (DBMSs) have emerged to support such tasks. It is unclear how well these systems support real-world image analysis use cases, and how performant the image analytics tasks implemented on top of such systems are. In this paper, we present the first comprehensive evaluation of large-scale image analysis systems using two real-world scientific image data processing use cases. We evaluate five representative systems (SciDB, Myria, Spark, Dask, and TensorFlow) and find that each of them has shortcomings that complicate implementation or hurt performance. Such shortcomings lead to new research opportunities in making large-scale image analysis both efficient and easy to use.},
	number = {11},
	urldate = {2018-04-18},
	journal = {Proc. VLDB Endow.},
	author = {Mehta, Parmita and Dorkenwald, Sven and Zhao, Dongfang and Kaftan, Tomer and Cheung, Alvin and Balazinska, Magdalena and Rokem, Ariel and Connolly, Andrew and Vanderplas, Jacob and AlSayyad, Yusra},
	month = aug,
	year = {2017},
	pages = {1226--1237}
}

@article{warren_liao_clustering_2005,
	title = {Clustering of {Time} {Series} {Data}-a {Survey}},
	volume = {38},
	issn = {0031-3203},
	url = {http://dx.doi.org/10.1016/j.patcog.2005.01.025},
	doi = {10.1016/j.patcog.2005.01.025},
	abstract = {Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research.},
	number = {11},
	urldate = {2018-04-18},
	journal = {Pattern Recogn.},
	author = {Warren Liao, T.},
	month = nov,
	year = {2005},
	keywords = {Clustering, Data mining, Distance measure, Time series data},
	pages = {1857--1874}
}

@article{kate_using_2016,
	title = {Using {Dynamic} {Time} {Warping} {Distances} {As} {Features} for {Improved} {Time} {Series} {Classification}},
	volume = {30},
	issn = {1384-5810},
	url = {http://dx.doi.org/10.1007/s10618-015-0418-x},
	doi = {10.1007/s10618-015-0418-x},
	abstract = {Dynamic time warping (DTW) has proven itself to be an exceptionally strong distance measure for time series. DTW in combination with one-nearest neighbor, one of the simplest machine learning methods, has been difficult to convincingly outperform on the time series classification task. In this paper, we present a simple technique for time series classification that exploits DTW's strength on this task. But instead of directly using DTW as a distance measure to find nearest neighbors, the technique uses DTW to create new features which are then given to a standard machine learning method. We experimentally show that our technique improves over one-nearest neighbor DTW on 31 out of 47 UCR time series benchmark datasets. In addition, this method can be easily extended to be used in combination with other methods. In particular, we show that when combined with the symbolic aggregate approximation (SAX) method, it improves over it on 37 out of 47 UCR datasets. Thus the proposed method also provides a mechanism to combine distance-based methods like DTW with feature-based methods like SAX. We also show that combining the proposed classifiers through ensembles further improves the performance on time series classification.},
	number = {2},
	urldate = {2018-04-18},
	journal = {Data Min. Knowl. Discov.},
	author = {Kate, Rohit J.},
	month = mar,
	year = {2016},
	keywords = {Dynamic time warping, Symbolic aggregate approximation, Time series classification},
	pages = {283--312}
}

@misc{noauthor_bag--features_nodate,
	title = {A {Bag}-of-{Features} {Framework} to {Classify} {Time} {Series}},
	url = {https://dl.acm.org/citation.cfm?id=2554080},
	urldate = {2018-04-08}
}

@inproceedings{osako_supervised_2017,
	title = {Supervised monaural source separation based on autoencoders},
	doi = {10.1109/ICASSP.2017.7951788},
	abstract = {In this paper, we propose a new supervised monaural source separation based on autoencoders. We employ the autoencoder for the dictionary training such that the nonlinear network can encode the target source with high expressiveness. The dictionary is trained by each target source without the mixture signal, which makes the system independent from the context where the dictionaries will be used. In separation process, the decoder portions of the trained autoencoders are used as dictionaries to find the activations in a iterative manner such that a summation of the decoder outputs approximates the original mixture. The results of the instruments source separation experiments revealed that the separation performance of the proposed method was superior to that of the NMF.},
	booktitle = {2017 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	author = {Osako, K. and Mitsufuji, Y. and Singh, R. and Raj, B.},
	month = mar,
	year = {2017},
	keywords = {Decoding, Dictionaries, Instruments, NMF, Separation processes, Source separation, Spectrogram, Training, autoencoder, autoencoders, dictionary training, matrix decomposition, neural networks, non-negative matrix factorization, nonlinear network, separation process, source separation, supervised monaural source separation},
	pages = {11--15}
}

@article{kondylakis_coconut:_2018,
	title = {Coconut: {A} {Scalable} {Bottom}-up {Approach} for {Building} {Data} {Series} {Indexes}},
	volume = {11},
	issn = {2150-8097},
	shorttitle = {Coconut},
	url = {https://doi.org/10.14778/3184470.3184472},
	doi = {10.14778/3184470.3184472},
	abstract = {Many modern applications produce massive amounts of data series that need to be analyzed, requiring efficient similarity search operations. However, the state-of-the-art data series indexes that are used for this purpose do not scale well for massive datasets in terms of performance, or storage costs. We pinpoint the problem to the fact that existing summarizations of data series used for indexing cannot be sorted while keeping similar data series close to each other in the sorted order. This leads to two design problems. First, traditional bulk-loading algorithms based on sorting cannot be used. Instead, index construction takes place through slow top-down insertions, which create a non-contiguous index that results in many random I/Os. Second, data series cannot be sorted and split across nodes evenly based on their median value; thus, most leaf nodes are in practice nearly empty. This further slows down query speed and amplifies storage costs. To address these problems, we present Coconut. The first innovation in Coconut is an inverted, sortable data series summarization that organizes data series based on a z-order curve, keeping similar series close to each other in the sorted order. As a result, Coconut is able to use bulk-loading techniques that rely on sorting to quickly build a contiguous index using large sequential disk I/Os. We then explore prefix-based and median-based splitting policies for bottom-up bulk-loading, showing that median-based splitting outperforms the state of the art, ensuring that all nodes are densely populated. Overall, we show analytically and empirically that Coconut dominates the state-of-the-art data series indexes in terms of construction speed, query speed, and storage costs.},
	number = {6},
	urldate = {2018-04-05},
	journal = {Proc. VLDB Endow.},
	author = {Kondylakis, Haridimos and Dayan, Niv and Zoumpatianos, Kostas and Palpanas, Themis},
	month = feb,
	year = {2018},
	pages = {677--690}
}

@misc{noauthor_cao_nodate,
	title = {Cao},
	url = {https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14559},
	urldate = {2018-04-03}
}

@misc{noauthor_cao_nodate-1,
	title = {Cao},
	url = {https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14559},
	urldate = {2018-04-03}
}

@misc{noauthor_cao_nodate-2,
	title = {Cao},
	url = {https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14559},
	urldate = {2018-04-03}
}

@inproceedings{xue_scalable_2017,
	address = {New York, NY, USA},
	series = {{WSDM} '17},
	title = {Scalable {Text} {Analysis}},
	isbn = {978-1-4503-4675-7},
	url = {http://doi.acm.org/10.1145/3018661.3022750},
	doi = {10.1145/3018661.3022750},
	abstract = {Latent Dirichlet Allocation (LDA) is an extremely popular probabilistic topic model used for a diverse class of appications. While highly effective, one important limitation of LDA is the high memory footprint of its inferencing algorithm, making it difficult to scale to a large dataset. In my thesis, I propose sdLDA, a highly-scalable disk-based LDA that (1) leverages the plentiful space available in disk to reduce its main-memory footprint and (2) preserves the sparsity of the extracted topics during sampling to improve both memory efficiency and sampling complexity of the inferencing algorithm. Our extensive experiments show that sdLDA scales to datasets that are two orders of magnitude larger than what existing main-memory-based algorithms can handle. Furthermore, sdLDA exhibits similar (or even better) performance compared to main-memory-based LDAs in terms of running time.},
	urldate = {2018-04-03},
	booktitle = {Proceedings of the {Tenth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}},
	publisher = {ACM},
	author = {Xue, Zijun},
	year = {2017},
	keywords = {scalability, text analysis, topic model},
	pages = {835--835}
}

@inproceedings{dietz_utilizing_2017,
	address = {New York, NY, USA},
	series = {{WSDM} '17},
	title = {Utilizing {Knowledge} {Graphs} in {Text}-centric {Information} {Retrieval}},
	isbn = {978-1-4503-4675-7},
	url = {http://doi.acm.org/10.1145/3018661.3022756},
	doi = {10.1145/3018661.3022756},
	abstract = {The past decade has witnessed the emergence of several publicly available and proprietary knowledge graphs (KGs). The increasing depth and breadth of content in KGs makes them not only rich sources of structured knowledge by themselves but also valuable resources for search systems. A surge of recent developments in entity linking and retrieval methods gave rise to a new line of research that aims at utilizing KGs for text-centric retrieval applications, making this an ideal time to pause and report current findings to the community, summarizing successful approaches, and soliciting new ideas. This tutorial is the first to disseminate the progress in this emerging field to researchers and practitioners. All tutorial resources are available online at http://github.com/laura-dietz/tutorial-utilizing-kg},
	urldate = {2018-04-03},
	booktitle = {Proceedings of the {Tenth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}},
	publisher = {ACM},
	author = {Dietz, Laura and Kotov, Alexander and Meij, Edgar},
	year = {2017},
	keywords = {document retrieval, entity linking, knowledge graphs, query understanding},
	pages = {815--816}
}

@inproceedings{mitra_neural_2017,
	address = {New York, NY, USA},
	series = {{WSDM} '17},
	title = {Neural {Text} {Embeddings} for {Information} {Retrieval}},
	isbn = {978-1-4503-4675-7},
	url = {http://doi.acm.org/10.1145/3018661.3022755},
	doi = {10.1145/3018661.3022755},
	abstract = {In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing tasks, such as language modelling and machine translation. This suggests that neural models will also achieve good performance on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using a semantic rather than lexical matching. Although initial iterations of neural models do not outperform traditional lexical-matching baselines, the level of interest and effort in this area is increasing, potentially leading to a breakthrough. The popularity of the recent SIGIR 2016 workshop on Neural Information Retrieval provides evidence to the growing interest in neural models for IR. While recent tutorials have covered some aspects of deep learning for retrieval tasks, there is a significant scope for organizing a tutorial that focuses on the fundamentals of representation learning for text retrieval. The goal of this tutorial will be to introduce state-of-the-art neural embedding models and bridge the gap between these neural models with early representation learning approaches in IR (e.g., LSA). We will discuss some of the key challenges and insights in making these models work in practice, and demonstrate one of the toolsets available to researchers interested in this area.},
	urldate = {2018-04-03},
	booktitle = {Proceedings of the {Tenth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}},
	publisher = {ACM},
	author = {Mitra, Bhaskar and Craswell, Nick},
	year = {2017},
	keywords = {information retrieval, neural networks, representation learning},
	pages = {813--814}
}

@misc{noauthor_related_nodate,
	title = {Related {Event} {Discovery}},
	url = {https://dl.acm.org/citation.cfm?id=3018713},
	urldate = {2018-04-03}
}

@inproceedings{xu_blockchain_2016,
	title = {The {Blockchain} as a {Software} {Connector}},
	doi = {10.1109/WICSA.2016.21},
	abstract = {Blockchain is an emerging technology for decentralized and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where components can find agreements on their shared states without trusting a central integration point or any particular participating components. Considering the blockchain as a software connector helps make explicitly important architectural considerations on the resulting performance and quality attributes (for example, security, privacy, scalability and sustainability) of the system. Based on our experience in several projects using blockchain, in this paper we provide rationales to support the architectural decision on whether to employ a decentralized blockchain as opposed to other software solutions, like traditional shared data storage. Additionally, we explore specific implications of using the blockchain as a software connector including design trade-offs regarding quality attributes.},
	booktitle = {2016 13th {Working} {IEEE}/{IFIP} {Conference} on {Software} {Architecture} ({WICSA})},
	author = {Xu, X. and Pautasso, C. and Zhu, L. and Gramoli, V. and Ponomarev, A. and Tran, A. B. and Chen, S.},
	month = apr,
	year = {2016},
	keywords = {Architecture connector, Australia, Blockchain, Connectors, Contracts, Cryptography, Design, Online banking, Peer-to-peer computing, Software, Trade-off, architectural decision, data handling, decentralized blockchain, decentralized data sharing, design trade-offs, distributed processing, distributed software architectures, quality attributes, software architecture, software connector, software quality, transactional data sharing, untrusted participants},
	pages = {182--191}
}

@inproceedings{eyal_bitcoin-ng:_2016,
	address = {Berkeley, CA, USA},
	series = {{NSDI}'16},
	title = {Bitcoin-{NG}: {A} {Scalable} {Blockchain} {Protocol}},
	isbn = {978-1-931971-29-4},
	shorttitle = {Bitcoin-{NG}},
	url = {http://dl.acm.org/citation.cfm?id=2930611.2930615},
	abstract = {Cryptocurrencies, based on and led by Bitcoin, have shown promise as infrastructure for pseudonymous online payments, cheap remittance, trustless digital asset exchange, and smart contracts. However, Bitcoinderived blockchain protocols have inherent scalability limits that trade off between throughput and latency, which withhold the realization of this potential. This paper presents Bitcoin-NG (Next Generation), a new blockchain protocol designed to scale. Bitcoin-NG is a Byzantine fault tolerant blockchain protocol that is robust to extreme churn and shares the same trust model as Bitcoin. In addition to Bitcoin-NG, we introduce several novel metrics of interest in quantifying the security and efficiency of Bitcoin-like blockchain protocols. We implement Bitcoin-NG and perform large-scale experiments at 15\% the size of the operational Bitcoin system, using unchanged clients of both protocols. These experiments demonstrate that Bitcoin-NG scales optimally, with bandwidth limited only by the capacity of the individual nodes and latency limited only by the propagation time of the network.},
	urldate = {2018-03-30},
	booktitle = {Proceedings of the 13th {Usenix} {Conference} on {Networked} {Systems} {Design} and {Implementation}},
	publisher = {USENIX Association},
	author = {Eyal, Ittay and Gencer, Adem Efe and Sirer, Emin Gün and Van Renesse, Robbert},
	year = {2016},
	pages = {45--59}
}
@article{eyal_bitcoin-ng:_nodate,
	title = {Bitcoin-{NG}: {A} {Scalable} {Blockchain} {Protocol}∗},
	abstract = {Cryptocurrencies, based on and led by Bitcoin, have shown promise as infrastructure for pseudonymous online payments, cheap remittance, trustless digital asset exchange, and smart contracts. However, Bitcoinderived blockchain protocols have inherent scalability limits that trade off between throughput and latency, which withhold the realization of this potential.},
	language = {en},
	author = {Eyal, Ittay and Emin, Adem Efe Gencer and Sirer, Gun},
	pages = {16}
}

@article{eyal_bitcoin-ng:_nodate-1,
	title = {Bitcoin-{NG}: {A} {Scalable} {Blockchain} {Protocol}∗},
	abstract = {Cryptocurrencies, based on and led by Bitcoin, have shown promise as infrastructure for pseudonymous online payments, cheap remittance, trustless digital asset exchange, and smart contracts. However, Bitcoinderived blockchain protocols have inherent scalability limits that trade off between throughput and latency, which withhold the realization of this potential.},
	language = {en},
	author = {Eyal, Ittay and Emin, Adem Efe Gencer and Sirer, Gun},
	pages = {16}
}

@inproceedings{kosba_hawk:_2016,
	title = {Hawk: {The} {Blockchain} {Model} of {Cryptography} and {Privacy}-{Preserving} {Smart} {Contracts}},
	shorttitle = {Hawk},
	doi = {10.1109/SP.2016.55},
	abstract = {Emerging smart contract systems over decentralized cryptocurrencies allow mutually distrustful parties to transact safely without trusted third parties. In the event of contractual breaches or aborts, the decentralized blockchain ensures that honest parties obtain commensurate compensation. Existing systems, however, lack transactional privacy. All transactions, including flow of money between pseudonyms and amount transacted, are exposed on the blockchain. We present Hawk, a decentralized smart contract system that does not store financial transactions in the clear on the blockchain, thus retaining transactional privacy from the public's view. A Hawk programmer can write a private smart contract in an intuitive manner without having to implement cryptography, and our compiler automatically generates an efficient cryptographic protocol where contractual parties interact with the blockchain, using cryptographic primitives such as zero-knowledge proofs. To formally define and reason about the security of our protocols, we are the first to formalize the blockchain model of cryptography. The formal modeling is of independent interest. We advocate the community to adopt such a formal model when designing applications atop decentralized blockchains.},
	booktitle = {2016 {IEEE} {Symposium} on {Security} and {Privacy} ({SP})},
	author = {Kosba, A. and Miller, A. and Shi, E. and Wen, Z. and Papamanthou, C.},
	month = may,
	year = {2016},
	keywords = {Contracts, Cryptographic protocols, Cryptography, Hawk programmer, Online banking, Privacy, aborts, automatic cryptographic protocol generation, blockchain model, compiler, contracts, contractual breach, cryptographic protocols, data privacy, decentralized blockchain, decentralized cryptocurrencies, decentralized smart contract system, electronic money, financial transactions, formal modeling, honest parties, money flow, mutually distrustful parties, privacy-preserving smart contracts, program compilers, transaction processing, transactional privacy, trusted computing, zero-knowledge proofs},
	pages = {839--858}
}

@article{christidis_blockchains_2016,
	title = {Blockchains and {Smart} {Contracts} for the {Internet} of {Things}},
	volume = {4},
	doi = {10.1109/ACCESS.2016.2566339},
	abstract = {Motivated by the recent explosion of interest around blockchains, we examine whether they make a good fit for the Internet of Things (IoT) sector. Blockchains allow us to have a distributed peer-to-peer network where non-trusting members can interact with each other without a trusted intermediary, in a verifiable manner. We review how this mechanism works and also look into smart contracts-scripts that reside on the blockchain that allow for the automation of multi-step processes. We then move into the IoT domain, and describe how a blockchain-IoT combination: 1) facilitates the sharing of services and resources leading to the creation of a marketplace of services between devices and 2) allows us to automate in a cryptographically verifiable manner several existing, time-consuming workflows. We also point out certain issues that should be considered before the deployment of a blockchain network in an IoT setting: from transactional privacy to the expected value of the digitized assets traded on the network. Wherever applicable, we identify solutions and workarounds. Our conclusion is that the blockchain-IoT combination is powerful and can cause significant transformations across several industries, paving the way for new business models and novel, distributed applications.},
	journal = {IEEE Access},
	author = {Christidis, K. and Devetsikiotis, M.},
	year = {2016},
	keywords = {Automation, Blockchain, Blockchains, Cryptography, Distributed processing, Internet of Things, Internet of things, IoT, Peer-to-peer computing, Privacy, blockchain, blockchain network, contracts, cryptographically verifiable manner, cryptography, distributed peer-to-peer network, distributed systems, internet of things, multistep process automation, peer-to-peer computing, smart contracts, transactional privacy, trusted computing, trusted intermediary},
	pages = {2292--2303}
}

@inproceedings{vo_can_2016,
	address = {New York, NY, USA},
	series = {{WebSci} '16},
	title = {Can {We} {Find} {Documents} in {Web} {Archives} {Without} {Knowing} {Their} {Contents}?},
	isbn = {978-1-4503-4208-7},
	url = {http://doi.acm.org/10.1145/2908131.2908165},
	doi = {10.1145/2908131.2908165},
	abstract = {Recent advances of preservation technologies have led to an increasing number of Web archive systems and collections. These collections are valuable to explore the past of the Web, but their value can only be uncovered with effective access and exploration mechanisms. Ideal search and ranking methods must be robust to the high redundancy and the temporal noise of contents, as well as scalable to the huge amount of data archived. Despite several attempts in Web archive search, facilitating access to Web archive still remains a challenging problem. In this work, we conduct a first analysis on different ranking strategies that exploit evidences from metadata instead of the full content of documents. We perform a first study to compare the usefulness of non-content evidences to Web archive search, where the evidences are mined from the metadata of file headers, links and URL strings only. Based on these findings, we propose a simple yet surprisingly effective learning model that combines multiple evidences to distinguish "good" from "bad" search results. We conduct empirical experiments quantitatively as well as qualitatively to confirm the validity of our proposed method, as a first step towards better ranking in Web archives taking metadata into account.},
	urldate = {2018-03-23},
	booktitle = {Proceedings of the 8th {ACM} {Conference} on {Web} {Science}},
	publisher = {ACM},
	author = {Vo, Khoi Duy and Tran, Tuan and Nguyen, Tu Ngoc and Zhu, Xiaofei and Nejdl, Wolfgang},
	year = {2016},
	keywords = {feature analysis, temporal ranking, web archive search},
	pages = {173--182}
}

@inproceedings{alam_web_2016,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Web {Archive} {Profiling} {Through} {Fulltext} {Search}},
	isbn = {978-3-319-43996-9 978-3-319-43997-6},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-43997-6_10},
	doi = {10.1007/978-3-319-43997-6_10},
	abstract = {An archive profile is a high-level summary of a web archive’s holdings that can be used for routing Memento queries to the appropriate archives. It can be created by generating summaries from the CDX files (index of web archives) which we explored in an earlier work. However, requiring archives to update their profiles periodically is difficult. Alternative means to discover the holdings of an archive involve sampling based approaches such as fulltext keyword searching to learn the URIs present in the response or looking up for a sample set of URIs and see which of those are present in the archive. It is the fulltext search based discovery and profiling that is the scope of this paper. We developed the Random Searcher Model (RSM) to discover the holdings of an archive by a random search walk. We measured the search cost of discovering certain percentages of the archive holdings for various profiling policies under different RSM configurations. We can make routing decisions of 80 \% of the requests correctly while maintaining about 0.9 recall by discovering only 10 \% of the archive holdings and generating a profile that costs less than 1 \% of the complete knowledge profile.},
	language = {en},
	urldate = {2018-03-23},
	booktitle = {Research and {Advanced} {Technology} for {Digital} {Libraries}},
	publisher = {Springer, Cham},
	author = {Alam, Sawood and Nelson, Michael L. and Sompel, Herbert Van de and Rosenthal, David S. H.},
	month = sep,
	year = {2016},
	pages = {121--132}
}

@inproceedings{kanhabua_how_2016,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {How to {Search} the {Internet} {Archive} {Without} {Indexing} {It}},
	isbn = {978-3-319-43996-9 978-3-319-43997-6},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-43997-6_12},
	doi = {10.1007/978-3-319-43997-6_12},
	abstract = {Significant parts of cultural heritage are produced on the web during the last decades. While easy accessibility to the current web is a good baseline, optimal access to the past web faces several challenges. This includes dealing with large-scale web archive collections and lacking of usage logs that contain implicit human feedback most relevant for today’s web search. In this paper, we propose an entity-oriented search system to support retrieval and analytics on the Internet Archive. We use Bing to retrieve a ranked list of results from the current web. In addition, we link retrieved results to the WayBack Machine; thus allowing keyword search on the Internet Archive without processing and indexing its raw archived content. Our search system complements existing web archive search tools through a user-friendly interface, which comes close to the functionalities of modern web search engines (e.g., keyword search, query auto-completion and related query suggestion), and provides a great benefit of taking user feedback on the current web into account also for web archive search. Through extensive experiments, we conduct quantitative and qualitative analyses in order to provide insights that enable further research on and practical applications of web archives.},
	language = {en},
	urldate = {2018-03-23},
	booktitle = {Research and {Advanced} {Technology} for {Digital} {Libraries}},
	publisher = {Springer, Cham},
	author = {Kanhabua, Nattiya and Kemkes, Philipp and Nejdl, Wolfgang and Nguyen, Tu Ngoc and Reis, Felipe and Tran, Nam Khanh},
	month = sep,
	year = {2016},
	pages = {147--160}
}

@article{alam_web_2016-1,
	title = {Web archive profiling through {CDX} summarization},
	volume = {17},
	issn = {1432-5012, 1432-1300},
	url = {https://link.springer.com/article/10.1007/s00799-016-0184-4},
	doi = {10.1007/s00799-016-0184-4},
	abstract = {With the proliferation of public web archives, it is becoming more important to better profile their contents, both to understand their immense holdings as well as to support routing of requests in the Memento aggregator. To save time, the Memento aggregator should only poll the archives that are likely to have a copy of the requested URI. Using the crawler index files produced after crawling, we can generate profiles of the archives that summarize their holdings and can be used to inform routing of the Memento aggregator’s URI requests. Previous work in profiling ranged from using full URIs (no false positives, but with large profiles) to using only top-level domains (TLDs) (smaller profiles, but with many false positives). This work explores strategies in between these two extremes. In our experiments, we correctly identified about 78 \% of the URIs that were present or not present in the archive with less than 1 \% relative cost as compared to the complete knowledge profile and 94 \% URIs with less than 10 \% relative cost without any false negatives. With respect to the TLD-only profile, the registered domain profile doubled the routing precision, while complete hostname and one path segment gave a tenfold increase in the routing precision.},
	language = {en},
	number = {3},
	urldate = {2018-03-23},
	journal = {International Journal on Digital Libraries},
	author = {Alam, Sawood and Nelson, Michael L. and Sompel, Herbert Van de and Balakireva, Lyudmila L. and Shankar, Harihar and Rosenthal, David S. H.},
	month = sep,
	year = {2016},
	pages = {223--238}
}

@article{alam_web_2016-2,
	title = {Web archive profiling through {CDX} summarization},
	volume = {17},
	issn = {1432-5012, 1432-1300},
	url = {https://link.springer.com/article/10.1007/s00799-016-0184-4},
	doi = {10.1007/s00799-016-0184-4},
	abstract = {With the proliferation of public web archives, it is becoming more important to better profile their contents, both to understand their immense holdings as well as to support routing of requests in the Memento aggregator. To save time, the Memento aggregator should only poll the archives that are likely to have a copy of the requested URI. Using the crawler index files produced after crawling, we can generate profiles of the archives that summarize their holdings and can be used to inform routing of the Memento aggregator’s URI requests. Previous work in profiling ranged from using full URIs (no false positives, but with large profiles) to using only top-level domains (TLDs) (smaller profiles, but with many false positives). This work explores strategies in between these two extremes. In our experiments, we correctly identified about 78 \% of the URIs that were present or not present in the archive with less than 1 \% relative cost as compared to the complete knowledge profile and 94 \% URIs with less than 10 \% relative cost without any false negatives. With respect to the TLD-only profile, the registered domain profile doubled the routing precision, while complete hostname and one path segment gave a tenfold increase in the routing precision.},
	language = {en},
	number = {3},
	urldate = {2018-03-23},
	journal = {International Journal on Digital Libraries},
	author = {Alam, Sawood and Nelson, Michael L. and Sompel, Herbert Van de and Balakireva, Lyudmila L. and Shankar, Harihar and Rosenthal, David S. H.},
	month = sep,
	year = {2016},
	pages = {223--238}
}

@inproceedings{fafalios_building_2017,
	title = {Building and {Querying} {Semantic} {Layers} for {Web} {Archives}},
	doi = {10.1109/JCDL.2017.7991555},
	abstract = {Web archiving is the process of collecting portions of the Web to ensure that the information is preserved for future exploitation. However, despite the increasing number of web archives worldwide, the absence of efficient and meaningful exploration methods still remains a major hurdle in the way of turning them into a usable and useful information source. In this paper, we focus on this problem and propose an RDF/S model and a distributed framework for building semantic profiles ("layers") that describe semantic information about the contents of web archives. A semantic layer allows describing metadata information about the archived documents, annotating them with useful semantic information (like entities, concepts and events), and publishing all this data on the Web as Linked Data. Such structured repositories offer advanced query and integration capabilities and make web archives directly exploitable by other systems and tools. To demonstrate their query capabilities, we build and query semantic layers for three different types of web archives. An experimental evaluation showed that a semantic layer can answer information needs that existing keyword-based systems are not able to sufficiently satisfy.},
	booktitle = {2017 {ACM}/{IEEE} {Joint} {Conference} on {Digital} {Libraries} ({JCDL})},
	author = {Fafalios, P. and Holzmann, H. and Kasturia, V. and Nejdl, W.},
	month = jun,
	year = {2017},
	keywords = {Buildings, Knowledge based systems, Linked Data, Metadata, Publishing, RDF/S model, Semantics, Web archive content, Web archiving, Web pages, distributed framework, information source, integration capability, meta data, metadata information, query capability, query processing, semantic Web, semantic information, semantic layer building, semantic layer querying, semantic profile building, structured repository},
	pages = {1--10}
}

@inproceedings{kishigami_blockchain-based_2015,
	title = {The {Blockchain}-{Based} {Digital} {Content} {Distribution} {System}},
	doi = {10.1109/BDCloud.2015.60},
	abstract = {The blockchain-based digital content distribution system was developed. Decentralized and pear-to-pear authentication mechanism can be considered as the ideal rights management mechanism. The blockchain has the potential to realize this ideal content distribution system. This is the successful model of the Superdistribution concept which was announced almost 30 years ago. The proposed system was demonstrated and got a lot of feedback for the future practical system.},
	booktitle = {2015 {IEEE} {Fifth} {International} {Conference} on {Big} {Data} and {Cloud} {Computing}},
	author = {Kishigami, J. and Fujimura, S. and Watanabe, H. and Nakadaira, A. and Akutsu, A.},
	month = aug,
	year = {2015},
	keywords = {Authentication, Blockchain, Content, Copyright protection, Distribution, Encryption, Metadata, Online banking, Rights, Servers, Superdistribution, blockchain-based digital content distribution system, content management, decentralized authentication mechanism, digital rights management, message authentication, peer-to-peer authentication mechanism, peer-to-peer computing, right management mechanism, superdistribution concept},
	pages = {187--190}
}

@inproceedings{lim_analysis_2014,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {The {Analysis} and {Countermeasures} on {Security} {Breach} of {Bitcoin}},
	isbn = {978-3-319-09146-4 978-3-319-09147-1},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-09147-1_52},
	doi = {10.1007/978-3-319-09147-1_52},
	abstract = {Due to the strength of bitcoin including the convenient payment and transfer, exchange into legal tender, low transfer fee, and others, its usage is increasing dramatically. Bitcoin is an e-money and virtual currency currently used as a means of payment in about 20,000 online companies and 1,000 offline stores as of Feb. 2014. Different from previous e-money, there is no issuing institution and it has trait of no regulation by countries or companies. However, as there is an increase in value of bitcoin followed by Cyprus financial crisis in past March, issues regarding the security breach on the rise. Since the exchange between legal tender and virtual currency is free in case of bitcoin, direct damage caused by security breach is very large. Also, as there is no governing institution, the user is responsible for all loss in case of damage occurrence. Therefore, the purpose of this study is to look into transaction method and security breach trend of bitcoin and its countermeasures.},
	language = {en},
	urldate = {2018-03-15},
	booktitle = {Computational {Science} and {Its} {Applications} – {ICCSA} 2014},
	publisher = {Springer, Cham},
	author = {Lim, Il-Kwon and Kim, Young-Hyuk and Lee, Jae-Gwang and Lee, Jae-Pil and Nam-Gung, Hyun and Lee, Jae-Kwang},
	month = jun,
	year = {2014},
	pages = {720--732}
}

@inproceedings{gervais_security_2016,
	address = {New York, NY, USA},
	series = {{CCS} '16},
	title = {On the {Security} and {Performance} of {Proof} of {Work} {Blockchains}},
	isbn = {978-1-4503-4139-4},
	url = {http://doi.acm.org/10.1145/2976749.2978341},
	doi = {10.1145/2976749.2978341},
	abstract = {Proof of Work (PoW) powered blockchains currently account for more than 90\% of the total market capitalization of existing digital cryptocurrencies. Although the security provisions of Bitcoin have been thoroughly analysed, the security guarantees of variant (forked) PoW blockchains (which were instantiated with different parameters) have not received much attention in the literature. This opens the question whether existing security analysis of Bitcoin's PoW applies to other implementations which have been instantiated with different consensus and/or network parameters. In this paper, we introduce a novel quantitative framework to analyse the security and performance implications of various consensus and network parameters of PoW blockchains. Based on our framework, we devise optimal adversarial strategies for double-spending and selfish mining while taking into account real world constraints such as network propagation, different block sizes, block generation intervals, information propagation mechanism, and the impact of eclipse attacks. Our framework therefore allows us to capture existing PoW-based deployments as well as PoW blockchain variants that are instantiated with different parameters, and to objectively compare the tradeoffs between their performance and security provisions.},
	urldate = {2018-03-15},
	booktitle = {Proceedings of the 2016 {ACM} {SIGSAC} {Conference} on {Computer} and {Communications} {Security}},
	publisher = {ACM},
	author = {Gervais, Arthur and Karame, Ghassan O. and Wüst, Karl and Glykantzis, Vasileios and Ritzdorf, Hubert and Capkun, Srdjan},
	year = {2016},
	keywords = {bitcoin, blockchain, performance, security},
	pages = {3--16}
}

@article{lin_survey_nodate,
	title = {A {Survey} of {Blockchain} {Security} {Issues} and {Challenges}},
	author = {Lin, Iuon-Chang and Liao, Tzu-Chun},
	pages = {7}
}

@article{yli-huumo_where_2016,
	title = {Where {Is} {Current} {Research} on {Blockchain} {Technology}?—{A} {Systematic} {Review}},
	volume = {11},
	issn = {1932-6203},
	shorttitle = {Where {Is} {Current} {Research} on {Blockchain} {Technology}?},
	url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0163477},
	doi = {10.1371/journal.pone.0163477},
	abstract = {Blockchain is a decentralized transaction and data management technology developed first for Bitcoin cryptocurrency. The interest in Blockchain technology has been increasing since the idea was coined in 2008. The reason for the interest in Blockchain is its central attributes that provide security, anonymity and data integrity without any third party organization in control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations. In this research, we have conducted a systematic mapping study with the goal of collecting all relevant research on Blockchain technology. Our objective is to understand the current research topics, challenges and future directions regarding Blockchain technology from the technical perspective. We have extracted 41 primary papers from scientific databases. The results show that focus in over 80\% of the papers is on Bitcoin system and less than 20\% deals with other Blockchain applications including e.g. smart contracts and licensing. The majority of research is focusing on revealing and improving limitations of Blockchain from privacy and security perspectives, but many of the proposed solutions lack concrete evaluation on their effectiveness. Many other Blockchain scalability related challenges including throughput and latency have been left unstudied. On the basis of this study, recommendations on future research directions are provided for researchers.},
	language = {en},
	number = {10},
	urldate = {2018-03-13},
	journal = {PLOS ONE},
	author = {Yli-Huumo, Jesse and Ko, Deokyoon and Choi, Sujin and Park, Sooyong and Smolander, Kari},
	month = oct,
	year = {2016},
	keywords = {Computer networks, Computer security, Cryptography, Database searching, Economics, Research integrity, Swans, Symposia},
	pages = {e0163477}
}

@inproceedings{vukolic_quest_2015,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {The {Quest} for {Scalable} {Blockchain} {Fabric}: {Proof}-of-{Work} vs. {BFT} {Replication}},
	isbn = {978-3-319-39027-7 978-3-319-39028-4},
	shorttitle = {The {Quest} for {Scalable} {Blockchain} {Fabric}},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-39028-4_9},
	doi = {10.1007/978-3-319-39028-4_9},
	abstract = {Bitcoin cryptocurrency demonstrated the utility of global consensus across thousands of nodes, changing the world of digital transactions forever. In the early days of Bitcoin, the performance of its probabilistic proof-of-work (PoW) based consensus fabric, also known as blockchain, was not a major issue. Bitcoin became a success story, despite its consensus latencies on the order of an hour and the theoretical peak throughput of only up to 7 transactions per second.The situation today is radically different and the poor performance scalability of early PoW blockchains no longer makes sense. Specifically, the trend of modern cryptocurrency platforms, such as Ethereum, is to support execution of arbitrary distributed applications on blockchain fabric, needing much better performance. This approach, however, makes cryptocurrency platforms step away from their original purpose and enter the domain of database-replication protocols, notably, the classical state-machine replication, and in particular its Byzantine fault-tolerant (BFT) variants.In this paper, we contrast PoW-based blockchains to those based on BFT state machine replication, focusing on their scalability limits. We also discuss recent proposals to overcoming these scalability limits and outline key outstanding open problems in the quest for the “ultimate” blockchain fabric(s).},
	language = {en},
	urldate = {2018-03-13},
	booktitle = {Open {Problems} in {Network} {Security}},
	publisher = {Springer, Cham},
	author = {Vukolić, Marko},
	month = oct,
	year = {2015},
	pages = {112--125}
}

@inproceedings{zyskind_decentralizing_2015,
	title = {Decentralizing {Privacy}: {Using} {Blockchain} to {Protect} {Personal} {Data}},
	shorttitle = {Decentralizing {Privacy}},
	doi = {10.1109/SPW.2015.27},
	abstract = {The recent increase in reported incidents of surveillance and security breaches compromising users' privacy call into question the current model, in which third-parties collect and control massive amounts of personal data. Bit coin has demonstrated in the financial space that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this paper, we describe a decentralized personal data management system that ensures users own and control their data. We implement a protocol that turns a block chain into an automated access-control manager that does not require trust in a third party. Unlike Bit coin, transactions in our system are not strictly financial – they are used to carry instructions, such as storing, querying and sharing data. Finally, we discuss possible future extensions to block chains that could harness them into a well-rounded solution for trusted computing problems in society.},
	booktitle = {2015 {IEEE} {Security} and {Privacy} {Workshops}},
	author = {Zyskind, G. and Nathan, O. and Pentland, A. '},
	month = may,
	year = {2015},
	keywords = {Compounds, Data privacy, Encryption, Online banking, Privacy, Protocols, auditable computing, automated access-control manager, bit coin, bitcoin, blockchain, data privacy, decentralized network, decentralized personal data management system, decentralizing privacy, financial space, personal data, personal data protection, privacy, public ledger, security breaches, surveillance, trusted computing, trusted computing problem, user privacy call},
	pages = {180--184}
}

@inproceedings{wang_human_2017,
	title = {Human {Resource} {Information} {Management} {Model} based on {Blockchain} {Technology}},
	doi = {10.1109/SOSE.2017.34},
	abstract = {The authenticity of human resource information has become an important factor that affects the cost and efficiency of human resource management. With the rapid development of mobile devices and Internet technology, various human resource risks caused by information asymmetry constantly bring economic loss, and even a fatal blow, to enterprises. Based on Blockchain(BC), this paper aims to combine traditional encryption technology with Internet-distributed technology, to establish a human resource information management model that reduces the risk of authenticity of human resource information. This model aims to solve the lack of discrimination of the authenticity of human resource information, and to provide authentic and effective decision support information to the human resource management of an organization. The operation method, such as Bitcoin, is used to certify the human resource documentations, as well as to bind the information and the documentation. Furthermore, human resource information is recorded in an accounting book based on BC technology; thus, modifying and validating may be difficult in the entire net work. Consensus mechanism, smart contract, accounting, and payment functions of BC can provide the basic support for human resource information management. Moreover, decentralization of the protection mechanism can be achieved to achieve low cost and high efficiency of information transfer, and to ensure a high-performance work system in the human resource management of enterprises.},
	booktitle = {2017 {IEEE} {Symposium} on {Service}-{Oriented} {System} {Engineering} ({SOSE})},
	author = {Wang, X. and Feng, L. and Zhang, H. and Lyu, C. and Wang, L. and You, Y.},
	month = apr,
	year = {2017},
	keywords = {Blockchain, Contracts, Encryption, Human resource management, Human resources, Information management, Internet, Internet technology, Internet-distributed technology, Organizations, Peer-to-peer computing, accounting book, blockchain technology, business data processing, consensus mechanism, decision support information, encryption technology, enterprise management, human resource information authenticity, human resource information management model, human resource management, information asymmetry, mobile computing, mobile devices, organisational aspects, payment functions, risk management, smart contract},
	pages = {168--173}
}

@article{deng_generalized_2018,
	title = {Generalized {Hidden}-{Mapping} {Minimax} {Probability} {Machine} for the training and reliability learning of several classical intelligent models},
	volume = {436-437},
	issn = {0020-0255},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025518300458},
	doi = {10.1016/j.ins.2018.01.034},
	abstract = {Minimax Probability Machine (MPM) is a binary classifier that optimizes the upper bound of the misclassification probability. This upper bound of the misclassification probability can be used as an explicit indicator to characterize the reliability of the classification model and thus makes the classification model more transparent. However, the existing related work is constrained to linear models or the corresponding nonlinear models by applying the kernel trick. To relax such constraints, we propose the Generalized Hidden-Mapping Minimax Probability Machine (GHM-MPM). GHM-MPM is a generalized MPM. It is capable of training many classical intelligent models, such as feedforward neural networks, fuzzy logic systems, and linear and kernelized linear models for classification tasks, and realizing the reliability learning of these models simultaneously. Since the GHM-MPM, similarly to the classical MPM, was originally developed only for binary classification, it is further extended to multi-class classification by using the obtained reliability indices of the binary classifiers of two arbitrary classes. The experimental results show that GHM-MPM makes the trained models more transparent and reliable than those trained by classical methods.},
	urldate = {2018-03-08},
	journal = {Information Sciences},
	author = {Deng, Zhaohong and Chen, Junyong and Zhang, Te and Cao, Longbing and Wang, Shitong},
	month = apr,
	year = {2018},
	keywords = {Classification, Fuzzy logical systems, Kernel tricks, Minimax probability, Neural networks, Reliability learning},
	pages = {302--319}
}

@article{wang_hybrid_2018,
	title = {A hybrid particle swarm optimization algorithm using adaptive learning strategy},
	volume = {436-437},
	issn = {0020-0255},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025518300380},
	doi = {10.1016/j.ins.2018.01.027},
	abstract = {Many optimization problems in reality have become more and more complex, which promote the research on the improvement of different optimization algorithms. The particle swarm optimization (PSO) algorithm has been proved to be an effective tool to solve various kinds of optimization problems. However, for the basic PSO, the updating strategy is mainly aims to learn the global best, and it often suffers premature convergence as well as performs poorly on many complex optimization problems, especially for multimodal problems. A hybrid PSO algorithm which employs an adaptive learning strategy (ALPSO) is developed in this paper. In ALPSO, we employ a self-learning based candidate generation strategy to ensure the exploration ability, and a competitive learning based prediction strategy to guarantee exploitation of the algorithm. To balance the exploration ability and the exploitation ability well, we design a tolerance based search direction adjustment mechanism. The experimental results on 40 benchmark test functions demonstrate that, compared with five representative PSO algorithms, ALPSO performs much better than the others in more cases, on both convergence accuracy and convergence speed.},
	urldate = {2018-03-08},
	journal = {Information Sciences},
	author = {Wang, Feng and Zhang, Heng and Li, Kangshun and Lin, Zhiyi and Yang, Jun and Shen, Xiao-Liang},
	month = apr,
	year = {2018},
	keywords = {Learning strategy, Multimodal optimization, Particle swarm optimization, Search direction},
	pages = {162--177}
}

@article{lopez-rubio_unsupervised_2018,
	title = {Unsupervised learning by cluster quality optimization},
	volume = {436-437},
	issn = {0020-0255},
	url = {http://www.sciencedirect.com/science/article/pii/S002002551830015X},
	doi = {10.1016/j.ins.2018.01.007},
	abstract = {Most clustering algorithms are designed to minimize a distortion measure which quantifies how far the elements of the clusters are from their respective centroids. The assessment of the results is often carried out with the help of cluster quality measures which take into account the compactness and separation of the clusters. However, these measures are not amenable to optimization because they are not differentiable with respect to the centroids even for a given set of clusters. Here we propose a differentiable cluster quality measure, and an associated clustering algorithm to optimize it. It turns out that the standard k-means algorithm is a special case of our method. Experimental results are reported with both synthetic and real datasets, which demonstrate the performance of our approach with respect to several standard quantitative measures.},
	urldate = {2018-03-08},
	journal = {Information Sciences},
	author = {López-Rubio, Ezequiel and Palomo, Esteban J. and Ortega-Zamorano, Francisco},
	month = apr,
	year = {2018},
	keywords = {Cluster quality measures, Clustering, K-means, Unsupervised learning},
	pages = {31--55}
}

@article{chen_disease_2018,
	title = {A disease diagnosis and treatment recommendation system based on big data mining and cloud computing},
	volume = {435},
	issn = {0020-0255},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025518300033},
	doi = {10.1016/j.ins.2018.01.001},
	abstract = {It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and cooperative actions in disease diagnoses and treatments between different departments and hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors might have difficulty in identifying them promptly and accurately. Therefore, to maximize the utilization of the advanced medical technology of developed hospitals and the rich medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in this paper. First, to effectively identify disease symptoms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm separately. The appropriate diagnosis and treatment schemes are recommended for patients and inexperienced doctors, even if they are in a limited therapeutic environment. Moreover, to reach the goals of high performance and low latency response, we implement a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experimental results demonstrate that the proposed DDTRS realizes disease-symptom clustering effectively and derives disease treatment recommendations intelligently and accurately.},
	urldate = {2018-03-08},
	journal = {Information Sciences},
	author = {Chen, Jianguo and Li, Kenli and Rong, Huigui and Bilal, Kashif and Yang, Nan and Li, Keqin},
	month = apr,
	year = {2018},
	keywords = {Big data mining, Cloud computing, Disease diagnosis and treatment, Recommendation system},
	pages = {124--149}
}

@article{mu_pearsons_2018,
	title = {A {Pearson}’s correlation coefficient based decision tree and its parallel implementation},
	volume = {435},
	issn = {0020-0255},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025517311763},
	doi = {10.1016/j.ins.2017.12.059},
	abstract = {In this paper, a Pearson’s correlation coefficient based decision tree (PCC-Tree) is established and its parallel implementation is developed in the framework of Map-Reduce (MR-PCC-Tree). The proposed methods employ Pearson’s correlation coefficient as a new measure of feature quality to confirm the optimal splitting attributes and splitting points in the growth of decision trees. Besides, the proposed MR-PCC-Tree adopts Map-Reduce technology to every component during the decision trees learning process for parallel computing, which mainly consists of a parallel Pearson’s correlation coefficient based splitting rule and a parallel splitting data method. The experimental analysis is conducted on a series of UCI benchmark data sets with different scales. In contrast to several traditional decision tree classifiers including BFT, C4.5, LAD, SC and NBT on 17 data sets, the proposed PCC-Tree is no worse than the traditional models as a whole. Furthermore, the experimental results on other 8 data sets show the feasibility of the proposed MR-PCC-Tree and its good parallel performance on reducing computational time for large-scale data classification problems.},
	urldate = {2018-03-08},
	journal = {Information Sciences},
	author = {Mu, Yashuang and Liu, Xiaodong and Wang, Lidong},
	month = apr,
	year = {2018},
	keywords = {Decision trees, Map-Reduce, Parallel computing, Pearson’s correlation coefficient},
	pages = {40--58}
}

@article{wang_effective_2018,
	title = {Effective lossless condensed representation and discovery of spatial co-location patterns},
	volume = {436-437},
	issn = {0020-0255},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025518300148},
	doi = {10.1016/j.ins.2018.01.011},
	abstract = {A spatial co-location pattern is a set of spatial features frequently co-occuring in nearby geographic spaces. Similar to closed frequent itemset mining, closed co-location pattern (CCP) mining was proposed for losslessly condensing large collections of prevalent co-location patterns. However, the state-of-the-art condensation methods in mining CCP are inspired by closed frequent itemset mining and do not consider the intrinsic characteristics of spatial co-locations, e.g., the participation index and ratio in spatial feature interactions, thus causing serious containment issues in CCP mining. In this paper, we propose a novel lossless condensed representation of prevalent co-location patterns, Super Participation Index-closed (SPI-closed) co-location. An efficient SPI-closed Miner is also proposed to effectively capture the nature of spatial co-location patterns, alongside the development of three additional pruning strategies to make the SPI-closed Miner efficient. This method captures richer feature interactions in spatial co-locations and solves the containment issues in existing CCP methods. A performance evaluation conducted on both synthetic and real-life data sets shows that SPI-closed Miner reduces the number of CCPs by up to 50\%, and runs much faster than the baseline CCP mining algorithm described in the literature.},
	urldate = {2018-03-08},
	journal = {Information Sciences},
	author = {Wang, Lizhen and Bao, Xuguang and Chen, Hongmei and Cao, Longbing},
	month = apr,
	year = {2018},
	keywords = {Lossless condensed representation, SPI-closed co-location patterns, Spatial co-location patterns, Spatial data mining},
	pages = {197--213}
}

@article{lopez-rubio_unsupervised_2018-1,
	title = {Unsupervised learning by cluster quality optimization},
	volume = {436-437},
	issn = {0020-0255},
	url = {http://www.sciencedirect.com/science/article/pii/S002002551830015X},
	doi = {10.1016/j.ins.2018.01.007},
	abstract = {Most clustering algorithms are designed to minimize a distortion measure which quantifies how far the elements of the clusters are from their respective centroids. The assessment of the results is often carried out with the help of cluster quality measures which take into account the compactness and separation of the clusters. However, these measures are not amenable to optimization because they are not differentiable with respect to the centroids even for a given set of clusters. Here we propose a differentiable cluster quality measure, and an associated clustering algorithm to optimize it. It turns out that the standard k-means algorithm is a special case of our method. Experimental results are reported with both synthetic and real datasets, which demonstrate the performance of our approach with respect to several standard quantitative measures.},
	urldate = {2018-03-08},
	journal = {Information Sciences},
	author = {López-Rubio, Ezequiel and Palomo, Esteban J. and Ortega-Zamorano, Francisco},
	month = apr,
	year = {2018},
	keywords = {Cluster quality measures, Clustering, K-means, Unsupervised learning},
	pages = {31--55}
}

@article{alajlouni_impact_2018,
	title = {Impact localization in dispersive waveguides based on energy-attenuation of waves with the traveled distance},
	volume = {105},
	issn = {0888-3270},
	url = {http://www.sciencedirect.com/science/article/pii/S0888327017306428},
	doi = {10.1016/j.ymssp.2017.12.007},
	abstract = {An algorithm is introduced to solve the general multilateration (source localization) problem in a dispersive waveguide. The algorithm is designed with the intention of localizing impact forces in a dispersive floor, and can potentially be used to localize and track occupants in a building using vibration sensors connected to the lower surface of the walking floor. The lower the wave frequencies generated by the impact force, the more accurate the localization is expected to be. An impact force acting on a floor, generates a seismic wave that gets distorted as it travels away from the source. This distortion is noticeable even over relatively short traveled distances, and is mainly caused by the dispersion phenomenon among other reasons, therefore using conventional localization/multilateration methods will produce localization error values that are highly variable and occasionally large. The proposed localization approach is based on the fact that the wave’s energy, calculated over some time window, decays exponentially as the wave travels away from the source. Although localization methods that assume exponential decay exist in the literature (in the field of wireless communications), these methods have only been considered for wave propagation in non-dispersive media, in addition to the limiting assumption required by these methods that the source must not coincide with a sensor location. As a result, these methods cannot be applied to the indoor localization problem in their current form. We show how our proposed method is different from the other methods, and that it overcomes the source-sensor location coincidence limitation. Theoretical analysis and experimental data will be used to motivate and justify the pursuit of the proposed approach for localization in a dispersive medium. Additionally, hammer impacts on an instrumented floor section inside an operational building, as well as finite element model simulations, are used to evaluate the performance of the algorithm. It is shown that the algorithm produces promising results providing a foundation for further future development and optimization.},
	urldate = {2018-01-27},
	journal = {Mechanical Systems and Signal Processing},
	author = {Alajlouni, Sa’ed and Albakri, Mohammad and Tarazaga, Pablo},
	month = may,
	year = {2018},
	keywords = {Dispersion, Impact localization, Multilateration, Occupant tracking},
	pages = {361--376}
}

@misc{gers_learning_2006,
	type = {letter},
	title = {Learning to {Forget}: {Continual} {Prediction} with {LSTM}},
	copyright = {© 2000 Massachusetts Institute of Technology},
	shorttitle = {Learning to {Forget}},
	url = {https://www.mitpressjournals.org/doi/abs/10.1162/089976600300015015},
	abstract = {{\textless}p{\textgreater}Long short-term memory (LSTM; Hochreiter \& Schmidhuber, 1997) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing continual input streams that are not a priori segmented into subsequences with explicitly marked ends at which the network's internal state could be reset. Without resets, the state may grow indefinitely and eventually cause the network to break down. Our remedy is a novel, adaptive “forget gate” that enables an LSTM cell to learn to reset itself at appropriate times, thus releasing internal resources. We review illustrative benchmark problems on which standard LSTM outperforms other RNN algorithms. All algorithms (including LSTM) fail to solve continual versions of these problems. LSTM with forget gates, however, easily solves them, and in an elegant way.{\textless}/p{\textgreater}},
	language = {en},
	urldate = {2018-01-26},
	author = {Gers, Felix A. and Schmidhuber, Jürgen and Cummins, Fred},
	month = mar,
	year = {2006},
	doi = {10.1162/089976600300015015}
}

@article{alajlouni_impact_2018-1,
	title = {Impact localization in dispersive waveguides based on energy-attenuation of waves with the traveled distance},
	volume = {105},
	issn = {0888-3270},
	url = {http://www.sciencedirect.com/science/article/pii/S0888327017306428},
	doi = {10.1016/j.ymssp.2017.12.007},
	abstract = {An algorithm is introduced to solve the general multilateration (source localization) problem in a dispersive waveguide. The algorithm is designed with the intention of localizing impact forces in a dispersive floor, and can potentially be used to localize and track occupants in a building using vibration sensors connected to the lower surface of the walking floor. The lower the wave frequencies generated by the impact force, the more accurate the localization is expected to be. An impact force acting on a floor, generates a seismic wave that gets distorted as it travels away from the source. This distortion is noticeable even over relatively short traveled distances, and is mainly caused by the dispersion phenomenon among other reasons, therefore using conventional localization/multilateration methods will produce localization error values that are highly variable and occasionally large. The proposed localization approach is based on the fact that the wave’s energy, calculated over some time window, decays exponentially as the wave travels away from the source. Although localization methods that assume exponential decay exist in the literature (in the field of wireless communications), these methods have only been considered for wave propagation in non-dispersive media, in addition to the limiting assumption required by these methods that the source must not coincide with a sensor location. As a result, these methods cannot be applied to the indoor localization problem in their current form. We show how our proposed method is different from the other methods, and that it overcomes the source-sensor location coincidence limitation. Theoretical analysis and experimental data will be used to motivate and justify the pursuit of the proposed approach for localization in a dispersive medium. Additionally, hammer impacts on an instrumented floor section inside an operational building, as well as finite element model simulations, are used to evaluate the performance of the algorithm. It is shown that the algorithm produces promising results providing a foundation for further future development and optimization.},
	urldate = {2018-01-26},
	journal = {Mechanical Systems and Signal Processing},
	author = {Alajlouni, Sa’ed and Albakri, Mohammad and Tarazaga, Pablo},
	month = may,
	year = {2018},
	keywords = {Dispersion, Impact localization, Multilateration, Occupant tracking},
	pages = {361--376}
}

@article{cao_integrated_2013,
	title = {Integrated {Oversampling} for {Imbalanced} {Time} {Series} {Classification}},
	volume = {25},
	issn = {1041-4347},
	doi = {10.1109/TKDE.2013.37},
	abstract = {This paper proposes a novel Integrated Oversampling (INOS) method that can handle highly imbalanced time series classification. We introduce an enhanced structure preserving oversampling (ESPO) technique and synergistically combine it with interpolation-based oversampling. ESPO is used to generate a large percentage of the synthetic minority samples based on multivariate Gaussian distribution, by estimating the covariance structure of the minority-class samples and by regularizing the unreliable eigen spectrum. To protect the key original minority samples, we use an interpolation-based technique to oversample a small percentage of synthetic population. By preserving the main covariance structure and intelligently creating protective variances in the trivial eigen dimensions, ESPO effectively expands the synthetic samples into the void area in the data space without being too closely tied with existing minority-class samples. This also addresses a key challenge for applying oversampling for imbalanced time series classification, i.e., maintaining the correlation between consecutive values through preserving the main covariance structure. Extensive experiments based on seven public time series data sets demonstrate that our INOS approach, used with support vector machines (SVM), achieved better performance over existing oversampling methods as well as state-of-the-art methods in time series classification.},
	number = {12},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	author = {Cao, H. and Li, X. L. and Woon, D. Y. K. and Ng, S. K.},
	month = dec,
	year = {2013},
	keywords = {Covariance matrix, ESPO, Eigenvalues and eigenfunctions, Gaussian distribution, INOS, Null space, Oversampling, SVM, Sampling methods, Support vector machines, Time series analysis, classification, covariance structure estimation, data mining, data space, eigen spectrum, enhanced structure preserving oversampling technique, imbalanced data, imbalanced time series classification, integrated oversampling method, interpolation, interpolation-based oversampling, learning, learning (artificial intelligence), machine learning, minority-class samples, multivariate Gaussian distribution, pattern classification, sampling methods, structure preserving, support vector machines, time series, time series data sets},
	pages = {2809--2822}
}

@article{rosenthal_medium-term_2017,
	title = {The medium-term prospects for long-term storage systems},
	volume = {35},
	issn = {0737-8831},
	url = {http://www.emeraldinsight.com/doi/full/10.1108/LHT-11-2016-0128},
	doi = {10.1108/LHT-11-2016-0128},
	number = {1},
	urldate = {2017-11-30},
	journal = {Library Hi Tech},
	author = {Rosenthal, David Stuart Holmes},
	month = mar,
	year = {2017},
	pages = {11--31}
}

@inproceedings{kelly_archival_2014,
	title = {The {Archival} {Acid} {Test}: {Evaluating} archive performance on advanced {HTML} and {JavaScript}},
	shorttitle = {The {Archival} {Acid} {Test}},
	doi = {10.1109/JCDL.2014.6970146},
	abstract = {When preserving web pages, archival crawlers sometimes produce a result that varies from what an end-user expects. To quantitatively evaluate the degree to which an archival crawler is capable of comprehensively reproducing a web page from the live web into the archives, the crawlers' capabilities must be evaluated. In this paper, we propose a set of metrics to evaluate the capability of archival crawlers and other preservation tools using the Acid Test concept. For a variety of web preservation tools, we examine previous captures within web archives and note the features that produce incomplete or unexpected results. From there, we design the test to produce a quantitative measure of how well each tool performs its task.},
	booktitle = {{IEEE}/{ACM} {Joint} {Conference} on {Digital} {Libraries}},
	author = {Kelly, M. and Nelson, M. L. and Weigle, M. C.},
	month = sep,
	year = {2014},
	keywords = {Browsers, Crawlers, Digital Preservation, HTML, Java, JavaScript, Libraries, Software, Standards, Web Archiving, Web Crawler, Web archives, Web pages, Web preservation tool, Web sites, acid test concept, archival acid test, archival crawlers, archive performance, hypermedia markup languages, information retrieval systems, preservation tools},
	pages = {25--28}
}

@article{brunelle_not_2015,
	title = {Not all mementos are created equal: measuring the impact of missing resources},
	volume = {16},
	issn = {1432-5012, 1432-1300},
	shorttitle = {Not all mementos are created equal},
	url = {https://link.springer.com/article/10.1007/s00799-015-0150-6},
	doi = {10.1007/s00799-015-0150-6},
	abstract = {Web archives do not always capture every resource on every page that they attempt to archive. This results in archived pages missing a portion of their embedded resources. These embedded resources have varying historic, utility, and importance values. The proportion of missing embedded resources does not provide an accurate measure of their impact on the Web page; some embedded resources are more important to the utility of a page than others. We propose a method to measure the relative value of embedded resources and assign a damage rating to archived pages as a way to evaluate archival success. In this paper, we show that Web users’ perceptions of damage are not accurately estimated by the proportion of missing embedded resources. In fact, the proportion of missing embedded resources is a less accurate estimate of resource damage than a random selection. We propose a damage rating algorithm that provides closer alignment to Web user perception, providing an overall improved agreement with users on memento damage by 17 \% and an improvement by 51 \% if the mementos have a damage rating delta {\textgreater}{\textgreater}{\textgreater}0.30. We use our algorithm to measure damage in the Internet Archive, showing that it is getting better at mitigating damage over time (going from a damage rating of 0.16 in 1998 to 0.13 in 2013). However, we show that a greater number of important embedded resources (2.05 per memento on average) are missing over time. Alternatively, the damage in WebCite is increasing over time (going from 0.375 in 2007 to 0.475 in 2014), while the missing embedded resources remain constant (13 \% of the resources are missing on average). Finally, we investigate the impact of JavaScript on the damage of the archives, showing that a crawler that can archive JavaScript-dependent representations will reduce memento damage by 13.5 \%.},
	language = {en},
	number = {3-4},
	urldate = {2017-10-31},
	journal = {International Journal on Digital Libraries},
	author = {Brunelle, Justin F. and Kelly, Mat and SalahEldeen, Hany and Weigle, Michele C. and Nelson, Michael L.},
	month = sep,
	year = {2015},
	pages = {283--301}
}

@article{alkwai_comparing_2017,
	title = {Comparing the {Archival} {Rate} of {Arabic}, {English}, {Danish}, and {Korean} {Language} {Web} {Pages}},
	volume = {36},
	issn = {1046-8188},
	url = {http://doi.acm.org/10.1145/3041656},
	doi = {10.1145/3041656},
	abstract = {It has long been suspected that web archives and search engines favor Western and English language webpages. In this article, we quantitatively explore how well indexed and archived Arabic language webpages are as compared to those from other languages. We began by sampling 15,092 unique URIs from three different website directories: DMOZ (multilingual), Raddadi, and Star28 (the last two primarily Arabic language). Using language identification tools, we eliminated pages not in the Arabic language (e.g., English-language versions of Aljazeera pages) and culled the collection to 7,976 Arabic language webpages. We then used these 7,976 pages and crawled the live web and web archives to produce a collection of 300,646 Arabic language pages. We compared the analysis of Arabic language pages with that of English, Danish, and Korean language pages. First, for each language, we sampled unique URIs from DMOZ; then, using language identification tools, we kept only pages in the desired language. Finally, we crawled the archived and live web to collect a larger sample of pages in English, Danish, or Korean. In total for the four languages, we analyzed over 500,000 webpages. We discovered: (1) English has a higher archiving rate than Arabic, with 72.04\% archived. However, Arabic has a higher archiving rate than Danish and Korean, with 53.36\% of Arabic URIs archived, followed by Danish and Korean with 35.89\% and 32.81\% archived, respectively. (2) Most Arabic and English language pages are located in the United States; only 14.84\% of the Arabic URIs had an Arabic country code top-level domain (e.g., sa) and only 10.53\% had a GeoIP in an Arabic country. Most Danish-language pages were located in Denmark, and most Korean-language pages were located in South Korea. (3) The presence of a webpage in a directory positively impacts indexing and presence in the DMOZ directory, specifically, positively impacts archiving in all four languages. In this work, we show that web archives and search engines favor English pages. However, it is not universally true for all Western-language webpages because, in this work, we show that Arabic webpages have a higher archival rate than Danish language webpages.},
	number = {1},
	urldate = {2017-10-31},
	journal = {ACM Trans. Inf. Syst.},
	author = {Alkwai, Lulwah M. and Nelson, Michael L. and Weigle, Michele C.},
	month = jun,
	year = {2017},
	keywords = {Arabic web, Danish web, English web, Korean web, Web archiving, digital preservation, indexing},
	pages = {1:1--1:34}
}

@inproceedings{poston_indoor_2016,
	title = {Indoor positioning from vibration localization in smart buildings},
	doi = {10.1109/PLANS.2016.7479722},
	abstract = {Indoor localization by means of a global navigation satellite system (GNSS) remains a difficult problem due to GNSS signal impairments created by the building's structure. This problem prompted the research community to devise many alternative techniques. Unfortunately, in order to locate persons indoors, these alternatives often require each person to carry some device to facilitate the localization process. This paper investigates the naturally-generated vibration signals from a person's footsteps as a potential source of information for indoor localization. Instrumenting a building with vibration sensors is a mature technology, but, historically, the role of the technology was measuring a building's response to external events (e.g., earthquakes), not for measuring occupant-generated vibrations. Some prior work studied outdoor detection of footsteps near borders or within restricted areas, but that environment and the localization objectives differed sufficiently from the scope of this research to limit the relevance of prior results. This paper reports on measurements from an instrumented, public building and examines viability of conventional localization algorithms for locating persons moving within a building. Noting the sub-optimum performance of these algorithms in this localization task, this paper proposes an extension to existing techniques to accommodate signal distortions encountered by vibrations in building structures.},
	booktitle = {2016 {IEEE}/{ION} {Position}, {Location} and {Navigation} {Symposium} ({PLANS})},
	author = {Poston, J. D. and Buehrer, R. M. and Woolard, A. G. and Tarazaga, P. A.},
	month = apr,
	year = {2016},
	keywords = {Concrete, Detectors, GNSS, Instruments, Slabs, Vibrations, buildings (structures), earthquakes, global navigation satellite system, indoor localization, indoor positioning, intelligent sensors, occupant-generated vibrations, outdoor footsteps detection, public building, smart buildings, vibration localization, vibration measurement, vibration sensors, wireless sensor networks},
	pages = {366--372}
}

@article{interlandi_titian:_2015,
	title = {Titian: {Data} {Provenance} {Support} in {Spark}},
	volume = {9},
	issn = {2150-8097},
	shorttitle = {Titian},
	url = {http://dx.doi.org/10.14778/2850583.2850595},
	doi = {10.14778/2850583.2850595},
	abstract = {Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today's DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence (e.g., from log files) and performing trial and error debugging. To aid this effort, we built Titian, a library that enables data provenance---tracking data through transformations---in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds---orders-of-magnitude faster than alternative solutions---while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30\% above the baseline job execution time.},
	number = {3},
	urldate = {2017-10-05},
	journal = {Proc. VLDB Endow.},
	author = {Interlandi, Matteo and Shah, Kshitij and Tetali, Sai Deep and Gulzar, Muhammad Ali and Yoo, Seunghyun and Kim, Miryung and Millstein, Todd and Condie, Tyson},
	month = nov,
	year = {2015},
	pages = {216--227}
}

@article{hoh_enhancing_2006,
	title = {Enhancing {Security} and {Privacy} in {Traffic}-{Monitoring} {Systems}},
	volume = {5},
	issn = {1536-1268},
	doi = {10.1109/MPRV.2006.69},
	abstract = {Intelligent transportation systems increasingly depend on probe vehicles to monitor traffic: they can automatically report position, travel time, traffic incidents, and road surface problems to a telematics service provider. This kind of traffic-monitoring system could provide good coverage and timely information on many more roadways than is possible with a fixed infrastructure such as cameras and loop detectors. This approach also promises significant reductions in infrastructure cost because the system can exploit the sensing, computing, and communications devices already installed in many modern vehicles. This architecture separates data from identities by splitting communication from data analysis. Data suppression techniques can help prevent data mining algorithms from reconstructing private information from anonymous database samples},
	number = {4},
	journal = {IEEE Pervasive Computing},
	author = {Hoh, Baik and Gruteser, M. and Xiong, Hui and Alrabady, A.},
	month = oct,
	year = {2006},
	keywords = {Cameras, Computerized monitoring, Information security, Intelligent transportation systems, Intelligent vehicles, Privacy, Probes, Road transportation, Road vehicles, Telematics, clustering, data analysis, data integrity, data mining, data mining algorithm, data privacy, data suppression technique, database management systems, intelligent transportation system, privacy, probe vehicle, road surface problem, security, security of data, telematics service provider database, traffic information systems, traffic monitoring systems, traffic-monitoring system},
	pages = {38--46}
}

@article{hoh_enhancing_2006-1,
	title = {Enhancing {Security} and {Privacy} in {Traffic}-{Monitoring} {Systems}},
	volume = {5},
	issn = {1536-1268},
	url = {http://dx.doi.org/10.1109/MPRV.2006.69},
	doi = {10.1109/MPRV.2006.69},
	abstract = {Future traffic-monitoring applications will be able to collect location traces from numerous GPS-equipped probe vehicles. This article discusses the privacy and data integrity challenges that this approach raises and presents an overview of architectural and algorithmic solutions. The proposed architecture splits communication and data analysis functions, thereby addressing privacy by separating data and identities. The authors also show that anonymous location samples stored in a database still let data mining algorithms reconstruct private information, such as home locations. Thus, a complete solution requires data suppression techniques to reduce these risks in addition to the proposed architecture. This article is part of a special issue on Intelligent Transportation.},
	number = {4},
	urldate = {2017-10-02},
	journal = {IEEE Pervasive Computing},
	author = {Hoh, Baik and Gruteser, Marco and Xiong, Hui and Alrabady, Ansaf},
	month = oct,
	year = {2006},
	keywords = {clustering, data integrity, privacy, security, traffic monitoring systems},
	pages = {38--46}
}

@inproceedings{kaupins_legal_2005,
	title = {Legal and {Ethical} {Implications} of {Employee} {Location} {Monitoring}},
	doi = {10.1109/HICSS.2005.388},
	abstract = {Location technologies allow employers to monitor the location of employees. The technologies range from global positioning systems able to determine outdoor locations worldwide to sensor networks able to determine locations within buildings. Few international laws and no American laws directly address location monitoring. International privacy laws, the Electronic Communications Privacy Act, the USA Patriot Act and other laws involving Internet and e-mail monitoring might provide the pattern for future location monitoring legislation. Ethical considerations such as privacy, accuracy, inconsistency, security, and reputation also may affect future legislation. In writing corporate policies governing location monitoring, the employer's business interests may outweigh an employee's privacy interest. However, privacy invasion may be considered when the employer's monitoring has been physically invasive and has no legitimate business purpose. Future research should investigate management and employee attitudes toward location monitoring and the pattern of location monitoring policies.},
	booktitle = {Proceedings of the 38th {Annual} {Hawaii} {International} {Conference} on {System} {Sciences}},
	author = {Kaupins, G. and Minch, R.},
	month = jan,
	year = {2005},
	keywords = {Electronic mail, Internet, Law, Legal factors, Legislation, Monitoring, Privacy, Security, Sensor systems, Writing},
	pages = {133a--133a}
}
@incollection{kizza_mobile_2013,
	series = {Texts in {Computer} {Science}},
	title = {Mobile {Systems} and {Their} {Intractable} {Social}, {Ethical} and {Security} {Issues}},
	isbn = {978-1-4471-4989-7 978-1-4471-4990-3},
	url = {https://link.springer.com/chapter/10.1007/978-1-4471-4990-3_14},
	abstract = {This chapter begins by presenting rather a frightening and quickly evolving mobile telecommunication and computing technologies; their unprecedented global reach and inclusion; unparalleled social, financial, and cultural prowess; and the yet to be defined social, moral, and ethical value systems. We discuss the crucial role of the mobile operating system and survey the current mobile devises and their anchoring mobile operating systems. The discussion then settles on the main themes of the book, that is, the ethical and security issues in the mobile ecosystem. We discuss the troubling issue of the mobile ecosystem, the location-based tracking system (LTS). This technology, in three types, is troubling because it is being used by merchants, law enforcement agencies and almost everyone else who needs to know where you and your smart electronic device are. We note that while users of smart devices may not be aware, these technologies have huge ethical and privacy implications. In our discussion, we focus on the security and threat to individual privacy, for even in public places individuals deserve some privacy, resulting from the collection, aggregation, and centralization of personal information, without user consent of the user and also in the absence of applicable laws. On security issues in the ecosystem, we note that mobile devices are increasingly holding and storing more private data like personal and business and they are roaming in public spaces on public networks with limited security and cryptographic protocols to protect the data. Also because of the fact that these devices have the ability to roam on several networks, there is a wider sphere of attack beset by geographical, legal, and moral differences. We note that the penetration trend of these smart mobile devices is not limited to faraway rural places but more scaring is their rapid penetration on enterprise IT spaces where security is paramount for any device.},
	language = {en},
	urldate = {2017-10-02},
	booktitle = {Ethical and {Social} {Issues} in the {Information} {Age}},
	publisher = {Springer, London},
	author = {Kizza, Joseph Migga},
	year = {2013},
	doi = {10.1007/978-1-4471-4990-3_14},
	pages = {281--297}
}

@inproceedings{wang_privacy_2009,
	title = {Privacy and ethical issues in location-based tracking systems},
	doi = {10.1109/ISTAS.2009.5155910},
	abstract = {Location-based tracking systems (LTSs) use a variety of technologies to record the locations of objects. An LTS can increase the risks to the privacy and security of individuals. Previous studies have failed to distinguish between losses and violations of privacy when the locations of individuals are recorded by an LTS. We argue that individual privacy is threatened not by the collection of public location information but by the centralization of aggregated information, and by the combination of location information with other personal information. Further, informed consent should be required when the collection of information might cause a violation of privacy.},
	booktitle = {2009 {IEEE} {International} {Symposium} on {Technology} and {Society}},
	author = {Wang, J. L. and Loui, M. C.},
	month = may,
	year = {2009},
	keywords = {Cellular phones, Finance, GSM, Global Positioning System, Information security, Law enforcement, Privacy, RFID tags, Satellite broadcasting, Vehicles, data privacy, ethical aspects, ethical issues, location-based tracking systems, mobile computing, privacy issues, public location information, tracking},
	pages = {1--4}
}

@article{hazas_location-aware_2004,
	title = {Location-aware computing comes of age},
	volume = {37},
	issn = {0018-9162},
	doi = {10.1109/MC.2004.1266301},
	abstract = {At the core of invisible computing is context awareness, the concept of sensing and reacting to dynamic environments and activities. Location is a crucial component of context, and much research in the past decade has focused on location-sensing technologies, location-aware application support, and location-based applications. With numerous factors driving deployment of sensing technologies, location-aware computing may soon become a part of everyday life.},
	number = {2},
	journal = {Computer},
	author = {Hazas, M. and Scott, J. and Krumm, J.},
	month = feb,
	year = {2004},
	keywords = {Application software, Bluetooth, Computer vision, Global Positioning System, Hardware, Military computing, Mobile handsets, Sensor fusion, Ultra wideband technology, Wireless sensor networks, dynamic activity, dynamic environment, invisible computing, location-aware application support, location-aware computing, location-based application, location-sensing technology, mobile computing, wireless LAN},
	pages = {95--97}
}

@inproceedings{olabarriaga_understanding_2013,
	address = {New York, NY, USA},
	series = {{WORKS} '13},
	title = {Understanding {Workflows} for {Distributed} {Computing}: {Nitty}-gritty {Details}},
	isbn = {978-1-4503-2502-8},
	shorttitle = {Understanding {Workflows} for {Distributed} {Computing}},
	url = {http://doi.acm.org/10.1145/2534248.2534255},
	doi = {10.1145/2534248.2534255},
	abstract = {Scientific workflow management is heavily used in our organization. After six years, a large number of workflows are available and regularly used to run biomedical data analysis experiments on distributed infrastructures, mostly on grids. In this paper we present our first efforts to better understand and characterise these workflows. We start with a set of considerations previously proposed in the literature (workflow dimensions and motifs), and revise these to more closely describe what we observe in our workflows. We conclude that workflow characteristics can be categorized at two levels: firstly, the features characterizing the distributed application and how to implement it as a workflow, and secondly, workflow motifs that depend on the features of the selected workflow management system. These characteristics could be useful in the future to understand a larger set of workflows and to identify functional requirements for further development workflow management systems.},
	urldate = {2017-09-21},
	booktitle = {Proceedings of the 8th {Workshop} on {Workflows} in {Support} of {Large}-{Scale} {Science}},
	publisher = {ACM},
	author = {Olabarriaga, Silvia D. and Jaghoori, Mohammad Mahdi and Korkhov, Vladimir and van Schaik, Barbera and van Kampen, Antoine},
	year = {2013},
	keywords = {distributed computing, e-science, scientific workflow, workflow development, workflow management},
	pages = {68--76}
}

@inproceedings{sundaravarathan_mewse:_2016,
	address = {Riverton, NJ, USA},
	series = {{CASCON} '16},
	title = {{MEWSE}: {Multi}-engine {Workflow} {Submission} and {Execution} on {Apache} {YARN}},
	shorttitle = {{MEWSE}},
	url = {http://dl.acm.org/citation.cfm?id=3049877.3049897},
	abstract = {In this era of Big Data, organizations are attempting to gain new insights from the data by performing more complex analyses often involving multiple data sets and multiple analytic techniques. Creating a workflow to perform theses analyses can involve several analytic engines and require multiple transformations of the data as it flows through the steps of the task. Coordinating the jobs involved in a workflow is handled by a workflow management system. Current workflow systems, however, typically run only on one engine and do not offer the versatility required by analytic workflows. On the other hand, the process of submitting the jobs on different engines manually can be time consuming and requires the expertise of working with the various analytic engines. In this paper we present MEWSE - Multi Engine Workflow Submission and Execution on Apache YARN. Users submit multi-engine analytic workflows specified using an XML-based description language and MEWSE manages the scheduling and execution of the workflow on top of a YARN cluster. MEWSE is designed with plug and play functionalities to allow the inclusion of new engines as required. MEWSE is demonstrated on Amazon EC2 with a sample workflow that includes a combination of Hadoop, Mahout, Java programs and scripts to process the data.},
	urldate = {2017-09-21},
	booktitle = {Proceedings of the 26th {Annual} {International} {Conference} on {Computer} {Science} and {Software} {Engineering}},
	publisher = {IBM Corp.},
	author = {Sundaravarathan, K. and Martin, P. and Rope, D. and McRoberts, M. and Statchuk, C.},
	year = {2016},
	keywords = {analytic workflows, apache YARN, big data, workflow},
	pages = {194--200}
}

@inproceedings{abu-dbai_enterprise_2016,
	address = {New York, NY, USA},
	series = {{SYSTOR} '16},
	title = {Enterprise {Resource} {Management} in {Mesos} {Clusters}},
	isbn = {978-1-4503-4381-7},
	url = {http://doi.acm.org/10.1145/2928275.2933272},
	doi = {10.1145/2928275.2933272},
	abstract = {Enterprise data centers increasingly adopt a cloud-like architecture that enables the execution of multiple workloads on a shared pool of resources, reduces the data center footprint and drives down the costs. A number of cluster resource managers have appeared over the last few years, aimed at providing a uniform technology-neutral resource representation and management substrate. Examples include Apache YARN, Google Borg and Omega, Apache Mesos, and IBM Platform EGO. The Apache Mesos project [2] is emerging as a leading open source resource management technology for server clusters. Mesos offers simple yet powerful and flexible APIs, highly available and fault tolerant architecture, scalability to large clusters, isolation between tasks using Linux containers, multi-dimensional resource scheduling, ability to allocate shares of the cluster to roles representing users or user groups, and a clear separation of concerns between the applications (termed frameworks) and the "cluster kernel", which is Mesos. The resource scheduler of Mesos supports a generalization of max-min fairness, termed Dominant Resource Fairness (DRF) [1] scheduling discipline, which allows to harmonize execution of heterogeneous workloads (in terms of resource demand) by maximizing the share of any resource allocated to a specific framework. However, the default Mesos allocation mechanism lacks a number of policy and tenancy capabilities, important in enterprise deployments. We have investigated integration of Mesos with the IBM EGO (enterprise grid orchestrator) technology [3] which underpins various high performance computing, analytics and big data clusters in a variety of industry verticals including financial services, life sciences, manufacturing and electronics. We have designed and implemented an experimental integration prototype, and have tested it with SparkBench workloads. We demonstrate how Mesos can be enriched with new resource policy capabilities, required for managing enterprise data centers, such as • Capturing of the hierarchical structure of an enterprise (organisations, departments, groups, teams, users) by defining the corresponding resource consumer tree; • A fine grained resource plan allowing to define resource share ratio, ownership and lending/borrowing policies for each resource consumer; • A rich set of resource management policies making use of the hierarchical resource consumer model and providing fairness and isolation to the members of hierarchy including an important ability to dynamically change the allocations (time-based policy); • A Web-based GUI providing a centralized console through which the whole cluster is observed and managed. In particular, the cluster-wide resource management policies are applied through this GUI.},
	urldate = {2017-09-21},
	booktitle = {Proceedings of the 9th {ACM} {International} on {Systems} and {Storage} {Conference}},
	publisher = {ACM},
	author = {Abu-Dbai, Abed and Breitgand, David and Gershinsky, Gidon and Glikson, Alex and Ahmed, Khalid},
	year = {2016},
	keywords = {cluster, resource management, resource pool},
	pages = {17:1--17:1}
}

@inproceedings{zheng_deploying_2017,
	address = {Piscataway, NJ, USA},
	series = {{CCGrid} '17},
	title = {Deploying {High} {Throughput} {Scientific} {Workflows} on {Container} {Schedulers} with {Makeflow} and {Mesos}},
	isbn = {978-1-5090-6610-0},
	url = {https://doi.org/10.1109/CCGRID.2017.9},
	doi = {10.1109/CCGRID.2017.9},
	abstract = {Workflows are a widely used abstraction for describing large scientific applications and running them on distributed systems. However, most workflow systems have been silent on the question of what execution environment each task in the workflow is expected to run in. Consequently, a workflow may run successfully in the environment it was created, but fail on other platforms due to the differences in execution environment. Container-based schedulers have recently arisen as a potential solution to this problem, adopting containers to distribute computing resources and deliver well-defined execution environments to applications. In this paper, we consider how to connect workflow system to container schedulers with minimal performance loss and higher system efficiency. As an example of current technology, we use Makeflow and Mesos. We present five design challenges, and address them by using four configurations that connecting workflow system to container scheduler from different level of the infrastructure. In order to take full advantage of the resource sharing schema of Mesos, we enable the resource monitor of Makeflow to dynamically update the task resource requirement. We explore the performance of a large bioinformatics workflow, and observe that using Makeflow, Work Queue and the Resource monitor together not only increase the transfer throughput but also achieves highest resource usage rate.},
	urldate = {2017-09-21},
	booktitle = {Proceedings of the 17th {IEEE}/{ACM} {International} {Symposium} on {Cluster}, {Cloud} and {Grid} {Computing}},
	publisher = {IEEE Press},
	author = {Zheng, Chao and Tovar, Ben and Thain, Douglas},
	year = {2017},
	keywords = {Container, Scheduling, Workflow},
	pages = {130--139}
}

@inproceedings{lee_experimental_2016,
	address = {New York, NY, USA},
	series = {{CIKM} '16},
	title = {An {Experimental} {Comparison} of {Iterative} {MapReduce} {Frameworks}},
	isbn = {978-1-4503-4073-1},
	url = {http://doi.acm.org/10.1145/2983323.2983647},
	doi = {10.1145/2983323.2983647},
	abstract = {MapReduce has become a dominant framework in big data analysis, and thus there have been significant efforts to implement various data analysis algorithms in MapReduce. Many data analysis algorithms are inherently iterative, repeating the same set of tasks until a convergence. To efficiently support iterative algorithms at scale, a few variants of Hadoop and new platforms have been proposed and actively developed in both academia and industry. Representative systems include HaLoop, iMapReduce, Twister, and Spark. In this paper, we experimentally compare Hadoop and the aforementioned systems using various workloads and metrics. The five systems are compared through four iterative algorithms---PageRank, recursive query, k-means, and logistic regression---on 50 Amazon EC2 machines (200 cores in total). We thoroughly explore the effectiveness of their new caching, communication, and scheduling mechanisms in support of iterative computation. Our evaluation also shows the performance depending on data skewness and memory residency. Overall, we believe that our evaluation and interpretation will be useful for designing a new framework or improving the existing ones.},
	urldate = {2017-09-20},
	booktitle = {Proceedings of the 25th {ACM} {International} on {Conference} on {Information} and {Knowledge} {Management}},
	publisher = {ACM},
	author = {Lee, Haejoon and Kang, Minseo and Youn, Sun-Bum and Lee, Jae-Gil and Kwon, YongChul},
	year = {2016},
	keywords = {benchmark, hadoop, iterative algorithms, mapreduce, spark},
	pages = {2089--2094}
}

@inproceedings{castro_classifying_2017,
	title = {Classifying {Short} {Unstructured} {Data} {Using} the {Apache} {Spark} {Platform}},
	doi = {10.1109/JCDL.2017.7991567},
	abstract = {People worldwide use Twitter to post updates about the events that concern them directly or indirectly. Study of these posts can help identify global events and trends of importance. Similarly, E-commerce applications organize their products in a way that can facilitate their management and satisfy the needs and expectations of their customers. However, classifying data such as tweets or product descriptions is still a challenge. These data are described by short texts, containing in their vocabulary abbreviations of sentences, emojis, hashtags, implicit codes, and other non-standard usage of written language. Consequently, traditional text classification techniques are not effective on these data. In this paper, we describe our use of the Spark platform to implement two classification strategies to process large data collections, where each datum is a short textual description. One of our solutions uses an associative classifier, while the other is based on a multiclass Logistic Regression classifier using Word2Vec as a feature selection and transformation technique. Our associative classifier captures the relationships among words that uniquely identify each class, and Word2Vec captures the semantic and syntactic context of the words. In our experimental evaluation, we compared our solutions, as well as Spark MLlib classifiers. We assessed effectiveness, efficiency, and memory requirements. The results indicate that our solutions are able to effectively classify the millions of data instances composed of thousands of distinct features and classes, found in our digital libraries.},
	booktitle = {2017 {ACM}/{IEEE} {Joint} {Conference} on {Digital} {Libraries} ({JCDL})},
	author = {Castro, E. P. S. and Chakravarty, S. and Williamson, E. and Pereira, D. A. and Fox, E. A.},
	month = jun,
	year = {2017},
	keywords = {Apache Spark platform, Data models, Libraries, Logistics, Machine learning algorithms, Spark MLlib classifiers, Sparks, Training, Twitter, Word2Vec, associative classifier, data collection processing, data instances, digital libraries, e-commerce applications, electronic commerce, emojis, feature selection, global event identification, hashtags, implicit codes, multiclass logistic regression classifier, natural language processing, pattern classification, pattern clustering, regression analysis, sentences, short textual description, short unstructured data classification, social networking (online), text classification techniques, transformation technique, vocabulary, vocabulary abbreviations, word semantic context, word syntactic context, written language nonstandard usage},
	pages = {1--10}
}

@inproceedings{holzmann_archivespark:_2016,
	address = {New York, NY, USA},
	series = {{JCDL} '16},
	title = {{ArchiveSpark}: {Efficient} {Web} {Archive} {Access}, {Extraction} and {Derivation}},
	isbn = {978-1-4503-4229-2},
	shorttitle = {{ArchiveSpark}},
	url = {http://doi.acm.org/10.1145/2910896.2910902},
	doi = {10.1145/2910896.2910902},
	abstract = {Web archives are a valuable resource for researchers of various disciplines. However, to use them as a scholarly source, researchers require a tool that provides efficient access to Web archive data for extraction and derivation of smaller datasets. Besides efficient access we identify five other objectives based on practical researcher needs such as ease of use, extensibility and reusability. Towards these objectives we propose ArchiveSpark, a framework for efficient, distributed Web archive processing that builds a research corpus by working on existing and standardized data formats commonly held by Web archiving institutions. Performance optimizations in ArchiveSpark, facilitated by the use of a widely available metadata index, result in significant speed-ups of data processing. Our benchmarks show that ArchiveSpark is faster than alternative approaches without depending on any additional data stores while improving usability by seamlessly integrating queries and derivations with external tools.},
	urldate = {2017-08-18},
	booktitle = {Proceedings of the 16th {ACM}/{IEEE}-{CS} on {Joint} {Conference} on {Digital} {Libraries}},
	publisher = {ACM},
	author = {Holzmann, Helge and Goel, Vinay and Anand, Avishek},
	year = {2016},
	keywords = {big data, data extraction, web archives},
	pages = {83--92}
}

@misc{noauthor_[libtd-872]_nodate,
	title = {[\#{LIBTD}-872] {Replicate} {Andrej}'s class project on your laptop},
	url = {https://webapps.es.vt.edu/jira/si/jira.issueviews:issue-html/LIBTD-872/LIBTD-872.html},
	urldate = {2017-08-17}
}