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2018年06月19日学术报告（Xun Zhou 美国爱荷华大学）
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发布时间：2018-06-15 18:25
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报告题目： Mining Spatio-Temporal Big Data for Urban Intelligence
报告时间： 2018年06月19日15点30分
报告地点： 计算机学院B403会议室
报告人： Xun Zhou
报告人国籍：中国
报告人单位： University of Iowa
报告人简介：
Dr. Xun Zhou is an Assistant Professor in the Department of Management Sciences, Tippie College of Business at the University of Iowa. He received a Ph.D. degree in Computer Science from the University of Minnesota, Twin Cities in 2014. Dr. Zhou’s research interests include big data management and analytics, spatial and spatio-temporal data mining, and Geographic Information Systems (GIS). His research has been published in top international conferences and journals such as KDD, ICDM, CIKM, and TKDE. He has received four best paper awards from conferences and workshops. Dr. Zhou is a recipient of the NSF CRII Award (2016). He is a co-editor-in-chief of Springer’s Encyclopedia of GIS, 2nd Edition. Dr. Zhou serves as a poster co-chair of ACM SIGSPATIAL GIS 2017-2018 and a co-chair of SSTD 2017 Early Career Researcher Workshop. He also serves on the program committees of leading conferences such as ICDM, CIKM, SIGSPATIAL GIS and a number of workshops.
报告摘要：
With the rapid development of mobile computing and sensing technologies, huge amounts of data with location and time information, such as GPS trajectories of vehicles, real-time traffic statistics, and geo-tagged social media posts, have been collected. These data, commonly referred to as Spatio-Temporal Big Data (STBD), have the potential to transform the society but also pose significant research challenges for data mining due to high data-variety and large candidate pattern cardinality. This talk presents example of STBD mining and analytics techniques, including (1) Urban event detection, (2) Taxi driving direction recommendations, and (3) Traffic accident prediction.
In the first example, a set of GPS trajectory data mining techniques will be introduced, which aim at quickly localizing urban events such as gathering of crowds, traffic congestion, and social unrests. This task is important to improving traffic efficiency and mitigating public safety risks. In the second example, a data-driven taxi driving direction recommendation technique is introduced. The technique aims to recommend better routes for drivers while they seek passengers to improve their income. Finally, a deep learning method for traffic accident prediction will be presented. Case studies and experiments for each example will be discussed as well.
邀请人：祝园园 副教授
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