Professor Dr. Qing Li


Keynote Speaker

Affiliation : Department of Computer Science, City University of Hong Kong

Country: Hong Kong

Title: Event Detection from Multi-Modal Big Data and Multi-Dimensional Analysis based on Event Cube



The Web 2.0 and the popularity of social media provide new opportunities and platforms for people to share their lessons, encounters, and fun experiences, generating massive amounts of data (a.k.a. Big Data). Such huge collections of big data capture our experiences and what’s around us, associated directly with various kinds of real-world concepts and events. The latter are often closely connected to people’s lives. Automatically detecting and analyzing those events from the massive online data, effectively and efficiently, not only can benefit the management of Big Data, but also facilitate “sensing the world” for both users and researchers. In the context of event detection and analysis (EDA) from social websites, the data resources appear to be in vast diversity of modalities, sources, semantics, and/or the variation of appearances. Such rich forms of data provides complementary and multi-perspective descriptions of the events, covering to various extent the six primary elements of an event, i.e., when, where, who, what, why, and how (or simply, 5W+1H). EDA from the Web data is however rather challenging, e.g., an event may be described by multimedia documents from multi-sourced data corpus, and the above-mentioned data variations may cause the tasks of EDA intractable. In this talk, we introduce techniques of discovering events from the multi-modal Big Data and building an event cube model to support event queries and analysis, by addressing the tasks of data cleansing, data fusion, event detection and modeling. Preliminary experimental results on some of the tasks will be reported. We further explore and connect the important events discovered in a multimodal collection of inputs from various public sources, uncover their co-occurrence and track down the spatial and temporal dependency to answer the challenging questions of "how" and "why". We envision that the “cooked data”, i.e., the set of detected events, and their intra-cube and inter-cube relationships extracted for various applications, will be useful to end-users in different domains such as detecting medical events, financial market crisis, and so on.

Qing Li is a Professor at the Department of Computer Science, and the Director of the Engineering Research Centre on Multimedia Software at the City University of Hong Kong, where he joined as a faculty member since Sept 1998. 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 multimedia retrieval and management, conceptual data modeling, social media and Web services, and e-learning systems. He has authored/co-authored over 300 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 and Knowledge Engineering (DKE), 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 ER, DASFAA, ICWL, ACM RecSys and IEEE U-MEDIA. Prof. Li is a Fellow of IET (UK), a senior member of IEEE (US) and a distinguished member of CCF (China).