Qing LI
Chair Professor and Head
Department of Computing
The Hong Kong Polytechnic University, Hong Kong

Qing Li is a Chair Professor at the Department of Computing, the Hong Kong Polytechnic University. He received his B.Eng. from Hunan University (Changsha), and M.Sc. and Ph.D. degrees from the University of Southern California (Los Angeles), all in computer science. His research interests include multi-modal data management, conceptual data modeling, social media, Web services, and e-learning systems. He has authored/co-authored over 400 publications in these areas. He is actively involved in the research community and has served as an associate editor of a number of major technical journals including IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Technology (TOIT), Data Science and Engineering (DSE), World Wide Web (WWW), and Journal of Web Engineering, in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits in the Steering Committees of DASFAA, ER, ACM RecSys, IEEE U-MEDIA, and ICWL. Prof. Li is a Fellow of IEE/IET (UK), and a distinguished member of CCF (China).

Event Modeling and Mining: Towards Explainable Events with a Plank Road

Recently, research on event management has redrawn much attention and made great progress. As the core tasks of event management, event modeling and mining are essential for accessing and utilizing events effectively. In this talk, we provide a detailed review of event modeling and event mining. Based on a general definition, different characteristics of events are described, along with the associated challenges. Then, we define four forms of events in order to better classify currently available but somewhat confusing event types; we also compare different event representation and relationship analysis techniques used for different forms of events. We present Event Cube as an example event model which, devised to accommodate multi-sourced event discovery and multi-dimensional analysis of events, represents a big step forward towards explainable events. Finally, we discuss several pending issues and application-specific challenges which also shed light on future research directions.