Browsing by Author "Lu, Wen-Fang"

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    MetroScope: An Advanced System for Real-Time Detection and Analysis of Metro-Related Threats and Events via Twitter
    He, Jianfeng; Wu, Syuan-Ying; Alhamadani, Abdulaziz; Chen, Chih-Fang; Lu, Wen-Fang; Lu, Chang-Tien; Solnick, David; Li, Yanlin (ACM, 2023-07-19)
    Metro systems are vital to our daily lives, but they face safety or reliability challenges, such as criminal activities or infrastructure disruptions, respectively. Real-time threat detection and analysis are crucial to ensure their safety and reliability. Although many existing systems use Twitter to detect metro-related threats or events in real-time, they have limitations in event analysis and system maintenance. Specifically, they cannot analyze event development, or prioritize events from numerous tweets. Besides, their users are required to continuously monitor system notifications, use inefficient content retrieval methods, and perform detailed system maintenance. We addressed those issues by developing the MetroScope system, a real-time threat/event detection system applied to Washington D.C. metro system. MetroScope can automatically analyze event development, prioritize events based on urgency, send emergency notifications via emails, provide efficient content retrieval, and self-maintain the system. Our MetroScope system is now available at http://orion.nvc.cs.vt.edu:5000/, with a video (https://www.youtube.com/watch?v=vKIK9M60-J8) introducing its features and instructions. MetroScope is a significant advancement in enhancing the safety and reliability of metro systems.
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    Real-time monitoring of volatile, retail investor-driven price fluctuations in the equity market
    Chang, Huai-Che; Pu, Layne; Mao, Wan-Yi; Lu, Wen-Fang (2023-05-07)
    There are some failing companies in the market, and people on Reddit look for stocks with high short interest because they are trying a short squeeze. For example, the netizens moved the Meta Materials (MMTLP) price from $1 to $12 before the bubble burst. Another example is the 2021 GameStop (GME) stock. The stock surge was a volatile and unprecedented event driven by a social media frenzy and short squeeze, leading to both gains and losses for investors. We hope that our project can shed more light on these stocks. This project shows the information of targeted tickers, and also applies sentiment analysis on the newest Reddit posts fetched by data streaming as a reference for investors. Apache Kafka is used to ingest the latest data for Spark, which is responsible for real-time data processing. Elasticsearch provides full-text search so users can filter posts by time and keywords. In this project, we proposed a method to delegate the offset values of Kafka brokers to Redis, so the system is resilient even all the machine are down. Our platform presents all the data like a BI tool. Users can find related news and latest Reddit posts from different sources at the same time, and therefore make more informed decisions.