讲座人：何静（博士、教授）

工作单位：澳大利亚 维多利亚大学 工程与科学学院

讲座时间：2016年2月25日下午3：00

讲座地点：李文正国际学术交流中心308报告厅

专家简介：Dr. Jing He is currently a full professor in the College of Engineering and Science at Victoria University, Australia. She was awarded a PhD degree from the Academy of Mathematics and System Science, Chinese Academy of Sciences in 2006. Prior to joining Victoria University, she worked at the 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 Systems, Scientific Workflow and some industry fields such as E-Health, Petroleum Exploration and Development, Water recourse Management and e-Research. She has published over 60 research papers in refereed international journals and conference proceedings, including ACM Transaction on Internet Technology (TOIT), IEEE Transaction on Knowledge and Data Engineering (TKDE), Information Systems, 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 has received over 1.5 million Australian dollar research funding from the 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.

主要内容：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 method for mining the anomalies of correlated multiple and co-evolving data streams in online and real-time manners. 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.