時間：6/4 (四) 3:30pm-5:00pm

地點：工程三館015室（地下室）

題目：Knowledge Processing with Big Data

主講者：Prof. Xindong Wu

 服務單位：University of Vermont, USA

Abstract:

Big Data processing concerns large-volume, growing data sets with multiple, heterogeneous, autonomous sources, and explores complex and evolving relationships among data objects. This talk starts with a HACE

Theorem (http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6547630) that characterizes the features of the Big Data revolution, and presents a Big-KE model, from the knowledge engineering perspective. We discuss challenging research issues in knowledge representation, knowledge-base construction, and knowledge services in the Big Data revolution.

Biography:

Xindong Wu is a Professor of Computer Science at the University of Vermont (USA), a Yangtze River Scholar in the School of Computer Science and Information Engineering at the Hefei University of Technology (China), and a Fellow of the IEEE and the AAAS. He holds a PhD in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, Big Data analytics, knowledge-based systems, and Web information exploration.

Dr. Wu is the founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), the founder and current Editor-in-Chief of Knowledge and Information Systems (KAIS, by Springer), the Founding Chair (2002-2006) of the IEEE Computer Society Technical Committee on Intelligent Informatics (TCII), and an Editor-in-Chief of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE, by the IEEE Computer Society) between January 1, 2005 and December 31, 2008. He has served as Program Committee Chair/Co-Chair for ICDM '03 (the 2003 IEEE International Conference on Data Mining), KDD-07 (the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), CIKM 2010 (the 19th ACM Conference on Information and Knowledge Management), and ASONAM 2014 (the 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining).(Home page: http://www.cs.uvm.edu/~xwu/home.html)