The Many Faces of Sequence Data Processing(计算机系讲座)
发布时间: 2016-04-11 10:21:35 浏览次数: 供稿:计算机系
演讲人:UMass Lowell大学Tingjian Ge教授
讲座时间:2016-04-11 10:21:35
讲座地点:信息楼417
讲座内容

 演讲人:UMass Lowell大学Tingjian Ge教授

讲座时间:4月20日(周三)14:00-15:30

讲座地点:信息楼四楼417

Title: The Many Faces of Sequence Data Processing

Abstract: Sequence data, also known as data streams, play an important role in data analytics research as well as Computer Science in general. Such data are prevalent: texts, biological sequences, ECG signals, time series, traffic sensory data, business and server logs, smartphone and social network data are just a few examples. In a broad sense, big data collected over time can be deemed as sequence data. A common type of analytical query over streams is pattern matching, also known as complex event processing.

A few complexities must be dealt with for real-world sequence data. For example, it may be produced at a high rate by unreliable devices and/or communicated through wireless networks (hence the data has noise). Moreover, patterns may need to take into consideration diverse semantics including parallel sub-patterns and graph structures. In this talk, I will describe a few lines of work we have carried out in the past few years on this topic. For pattern semantics, I discuss a few variants: subsequences, extended regular expressions, parallel and interleaving patterns, and subgraph-with-timing patterns. I also describe some algorithmic techniques to efficiently match the complex events in real time.

 

Bio: Tingjian Ge is an associate professor in the Computer Science Department of the University of Massachusetts, Lowell. He received a Ph.D. from Brown University in 2009. Prior to that, he got his Bachelor’s and Master’s degrees in Computer Science from Tsinghua University and UC Davis, respectively, and worked at Informix and IBM in California for six years. From 2009 to 2011 he worked as an assistant professor at the University of Kentucky. His research areas are in data management and big data analytics, with topics including noisy and uncertain data, data streams, and data security and privacy. He is a recipient of the NSF CAREER Award in 2012, and a Teaching Excellence Award at UMass Lowell in 2014. He often serves as a Program Committee member in major database and data mining conferences such as SIGMOD, ICDE, VLDB, and ICDM, and served as the Program Chair of New England Database Day 2015.

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