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 BigKE, a big data knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demanddriven knowledge navigation. We discuss challenging issues and our ongoing research efforts with BigKE.

Biography: Xindong Wu is a Professor in the School of Computing and Informatics at the University of Louisiana at Lafayette (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 engineering, and Web systems. He 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).