Title：社会系统的研究方法与问题

Speaker：王捷教授 斯坦福大学可持续发展中心执行主任

Time：9:00 a.m. May 16 2014 ( Friday )

Location：Room N224, South Building

Abstract: Detecting/predicting anomalies from multiple correlated data streams is valuable to those applications where a credible real-time event prediction system will minimise 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 analysing differences, changes, and trends in correlated multiple data streams. The predicted anomalies often indicate the critical and actionable information in several application domains.