Browsing by Author "Maharshi, Shivam"
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- Performance Measurement and Analysis of Transactional Web ArchivingMaharshi, Shivam (Virginia Tech, 2017-07-19)Web archiving is necessary to retain the history of the World Wide Web and to study its evolution. It is important for the cultural heritage community. Some organizations are legally obligated to capture and archive Web content. The advent of transactional Web archiving makes the archiving process more efficient, thereby aiding organizations to archive their Web content. This study measures and analyzes the performance of transactional Web archiving systems. To conduct a detailed analysis, we construct a meaningful design space defined by the system specifications that determine the performance of these systems. SiteStory, a state-of-the-art transactional Web archiving system, and local archiving, an alternative archiving technique, are used in this research. We experimentally evaluate the performance of these systems using the Greek version of Wikipedia deployed on dedicated hardware on a private network. Our benchmarking results show that the local archiving technique uses a Web server’s resources more efficiently than SiteStory for one data point in our design space. Better performance than SiteStory in such scenarios makes our archiving solution favorable to use for transactional archiving. We also show that SiteStory does not impose any significant performance overhead on the Web server for the rest of the data points in our design space.
- Solr Project with IDEAL, in CS5604 (Information Storage and Retrieval)Xia, Long; Jiang, Tingting; Galad, Andrej; Maharshi, Shivam (2016-05-04)This submission describes the work of the Solr team as part of the IDEAL project with the main goal of designing and developing a distributed search infrastructure. It includes the project reports, final presentations, as well as the solutions (configuration files & Java code) developed. The main responsibility of our team was to configure Near Real Time Indexing and implement Custom Ranking for tweets and web page collections. The idea behind NRT Indexing is to help perform incremental updates from an HBase table into the Solr index, thereby optimizing time utilized and compute resources. The main motivation behind the Custom Ranking solution is to improve system precision and recall by transforming user queries with the use of the metadata provided by the other teams. The implementation leverages these three techniques: Query Expansion, Psuedo Relevance Feedback and Query Boosting. Throughout the semester we closely collaborated with several other teams both in getting requirements and the input data.