【1月9日】【管理科学与工程学院学术论坛】Abnormality Detection in Co-evolving Data Streams发布日期：2019-09-12 21:09:41
Dr. Jing He is currently an associate professor in college of engineering and science at Victoria University, Australia. She has been awarded a PhD degree from Academy of Mathematics and System Science, Chinese Academy of Sciences in 2006. Prior to joining to Victoria University, she worked in University of Chinese Academy of Sciences, China during 2006-2008. She has been active in areas of Data Mining, Web service/Web search, Spatial and Temporal Database, Multiple Criteria Decision Making, Intelligent System, Scientific Workflow and some industry field such as E-Health, Petroleum Exploration and Development, Water recourse Management and e-Research. She has published over 60 research papers in the refereed international journals and conference proceedings including ACM transaction on Internet Technology (TOIT), IEEE Transaction on Knowledge and Data Engineering (TKDE), Information System, The Computer Journal, Computers and Mathematics with Applications, Concurrency and Computation: Practice and Experience, International Journal of Information Technology & Decision Making, Applied Soft Computing, and Water Resource Management. She received over 1.5 million Australia dollar research funding from Australian Research Council (ARC) with ARC early career researcher award (DECRA), ARC discovery project, ARC Linkage project and National Natural Science Foundation of China (NSFC) since 2008.
Detecting/predicting anomalies from multiple correlated data streams is valuable to those applications where a credible real-time event prediction system will minimize economic losses (e.g. stock market crash) and save lives (e.g. medical surveillance in the operating theatre). This talk will introduce an effective and efficient methods for mining the anomalies of correlated multiple and co-evolving data streams in online and real-time manner. It includes the detection/prediction of anomalies by analyzing differences, changes, and trends in correlated multiple data streams. The predicted anomalies often indicate the critical and actionable information in several application domains.