Title: Data Mining with Big Data


Big Data concerns large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and social sciences. This talk introduces a HACE theorem that characterizes the features of the Big Data revolution, and discusses a Big Data processing model, from the data mining perspective. Our recent research efforts in mining streaming data and streaming features are also presented in the Big Data context.


Xindong Wu is a Professor of Computer Science at the University of Vermont and a Fellow of 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 Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), Editor-in-Chief of Knowledge and Information Systems (KAIS, by Springer), and Editor-in-Chief of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He was Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE, by the IEEE Computer Society) between 2005 and 2008. He is the 2004 ACM SIGKDD Service Award winner and the 2006 IEEE ICDM Outstanding Service Award winner. He received the 2012 IEEE Computer Society Technical Achievement Award "for pioneering contributions to data mining and applications", and the 2014 IEEE ICDM 10-Year Highest Impact Paper Award.

Dr Wu