报告题目：On Fusion of Heterogeneous Data Sources
报告人：Philip S. Yu（Fellow of the ACM and IEEE，Professor，Department of Computer Science，University of Illinois at Chicago，U.S.A.）
The problem of big data has become increasingly importance in recent years. On the one hand, big data is an asset that potentially can offer tremendous value or reward to the data owners. On the other hand, it poses tremendous challenges to distil the value out of the big data. The very nature of big data poses challenges not only due to its volume, and velocity of being generated, but also its variety, where variety means the data can be collected from various sources with different formats from structured data to text to network/graph data, etc. In this talk, we focus on the variety issue and discuss the recent development in fusion of information from multiple data sources, which can be applied to multiple applications and disciplines. As the number and variety of social networks aimed at different purposes increase rapidly from Facebook to Twitter to Foursquare to Whatsapp to Instagram, users nowadays are participated in multiple online networks simultaneously to enjoy various services. How to fuse information spreading across multiple networks to achieve better understanding of customers and provide higher quality of services becomes the Holy Grail. Social networks will be used as an example to explain how to address the data fusion issue.
Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. He is a Fellow of the ACM and IEEE.
Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data”, and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 950 referred conference and journals papers cited more than 73,800 times with an H-index of 127. He has applied for more than 300 patents.
Dr. Yu is the Editor-in- Chief of ACM Transactions on Knowledge Discovery from Data. He is on the steering committee of ACM Conference on Information and Knowledge Management and was a steering committee member of the IEEE Data Engineering and the IEEE Data Mining Conference. He was the Editor-in- Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). He received the ICDM 2013 10-year Highest-Impact Paper Award, and the EDBT Test of Time Award (2014). He was an IBM Master Inventor. Dr. Yu received his PhD from Stanford University.