Scalable OLAP and Mining of Information Networks
Authors
- Jiawei Han (Univ. of Illinois, USA)
- Xifeng Yan (IBM T.J. Watson Research Center, USA)
- Philip Yu (University of Illinois at Chicago, USA)
Abstract
With the ubiquity of information networks and their broad applications, there have been numerous studies on the construction, online analytical processing, and mining of information networks in multiple disciplines, including social network analysis, World-Wide Web, database systems, data mining, machine learning, and networked communication and information systems. Moreover, with a great demand of research in this direction, there is a need of a systematic introduction of methods for analysis of information networks from multiple disciplines. In this tutorial, we will present an organized picture on scalable OLAP (online analytical processing) and mining of information networks, with the inclusion of the following topics: (1) an introduction to information networks and information network analysis, (2) general statistical behavior of information networks, (3) mining frequent subgraphs in large graphs and networks, (4) data integration, data cleaning and data validation in information networks, (5) clustering graphs and information networks, (6) classification of graphs and information networks; (7) summarization and simplification of graphs and information networks, (8) OLAP and multi-dimensional analysis of information networks, (9) evolution of dynamic information networks, and (10) research challenges on OLAP and mining of information networks.
About the Authors
Jiawei Han (Univ. of Illinois, USA)
Jiawei Han is a Professor in the Department of Computer Science at the University of Illinois. He has been working on research into data mining, data warehousing, stream data mining, spatiotemporal and multimedia data mining, biological data mining, social network analysis, text and Web mining, and software bug mining, with over 400 conference and journal publications. He has chaired or served in over 100 program committees of international conferences and workshops and also served or is serving on the editorial boards for Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Journal of Computer Science and Technology, and Journal of Intelligent Information Systems. He is currently the founding Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data (TKDD). Jiawei has received three IBM Faculty Awards, the Outstanding Contribution Award at the International Conference on Data Mining (2002), ACM Service Award (1999) and ACM SIGKDD Innovation Award (2004), and IEEE Computer Society Technical Achievement Award (2005). He is an ACM and IEEE Fellow. His book "Data Mining: Concepts and Techniques" (Morgan Kaufmann) has been used popularly as a textbook.
Xifeng Yan (IBM T.J. Watson Research Center, USA)
Dr. Xifeng Yan is an assistant professor at the University of California at Santa Barbara and also holds the Venkatesh “Venky” Narayanamurti Chair in Computer Science. He received a PhD degree in Computer Science from the University of Illinois at Urbana-Champaign in 2006. He was a research staff member at the IBM T. J. Watson Research Center between 2006 and 2008. Dr. Yan's research interests include data mining, databases, and bioinformatics. He has filed 6 patents and published more than 40 papers in refereed journals and conferences. Dr. Yan received the 2007 ACM SIGMOD Doctoral Dissertation Runner-Up Award for his work in graph mining and graph data management.
Philip Yu (University of Illinois at Chicago, USA)
Philip S. Yu received the M.S. and Ph.D. degrees in E.E. from Stanford University, and the M.B.A. degree from New York University. He is a Professor in the Department of Computer Science at the University of Illinois at Chicago and also the IBM Thomas J. Watson Research Center. His research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing, and performance modeling. Dr. Yu has published more than 520 papers in refereed journals and conferences. He holds or has applied for more than 300 US patents. Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He is associate editors of ACM Transactions on the Internet Technology and ACM Transactions on Knowledge Discovery from Data. He is on the steering committee of IEEE Conference on Data Mining and was a member of the IEEE Data Engineering steering committee. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). He has received several IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 94th plateau of Invention Achievement Awards. He was an IBM Master Inventor. Dr. Yu received a Research Contributions Award from IEEE Intl. Conference on Data Mining in 2003 and also an IEEE Region 1 Award for "promoting and perpetuating numerous new electrical engineering concepts" in 1999.
Session
EDBT Tutorial: Scalable OLAP and Mining of Information Networks (Thursday, March 26, 11:00—12:30)