Fast mining of complex time-stamped events

Given a collection of complex, time-stamped events, how do we find patterns and anomalies? Events could be meetings with one or more persons with one or more agenda items at zero or more locations (e.g., teleconferences), or they could be publications with authors, keywords, publishers, etc. In such settings, we want to solve the following problems: (1) find time stamps that look similar to each other and group them; (2) find anomalies; (3) provide interpretations of the clusters and anomalies by annotating them; (4) automatically find the right time-granularity in which to do analysis. Moreover, we want fast, scalable algorithms for all these problems. We address the above challenges through two main ideas. The first (T3) is to turn the problem into a graph analysis problem, by carefully treating each time stamp as a node in a graph. This viewpoint brings to bear the vast machinery of graph analysis methods (PageRank, graph partitioning, proximity analysis, and CenterPiece Subgraphs,...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Hanghang Tong, Yasushi Sakurai, Tina Eliassi-Rad, Christos Faloutsos
• Keywords: CIKM 2008, Graph Analysis, Graph Analysis Problem, Information Management, Time Stamps