TUTORIALS

"Mining Moving Object, Trajectory and Traffic Data"   [SLIDES]

Jiawei Han (Univ. of Illinois at Urbana-Champaign, USA),
Zhenhui Li (Univ. of Illinois at Urbana-Champaign, USA),
Lu An Tang (Univ. of Illinois at Urbana-Champaign, USA)
   
Abstract

With the wide availability of satellite, RFID, GPS, sensor, wireless, and video technologies, moving object data has been collected in massive scale and is becoming increasingly rich, complex, and ubiquitous. There is an imminent need for scalable and flexible data analysis over moving-object information; and thus mining moving-object data has become one of major challenges in data mining. There have been considerable research efforts on data mining for moving object, trajectory, and traffic data sets. However, there has been few systematic tutorial on knowledge discovery from such moving-object data sets. This tutorial presents a comprehensive, organized, and state-of-the-art survey on methodologies and algorithms on analyzing different kinds of moving-object data sets, with an emphasis on several important mining tasks: pattern-mining, clustering, classification, outlier analysis, and multidimensional analysis. Besides a thorough survey of the recent research work on this topic, we also show how real-world applications can benefit from data mining of moving object, trajectory, and traffic data sets. The tutorial consists of three parts: (1) trajectory data mining, (2) moving object pattern mining, and (3) traffic data mining. In the first part, clustering, classification, and outlier detection for trajectory data are explored. In the second part, mining various patterns for moving object data are surveyed. In the third part, route discovery, destination prediction, and hot-route or outlier detection for traffic data are explored. This tutorial is prepared for data mining, database, and machine learning researchers who are interested in moving object data analysis.

Biography

Jiawei Han, www.cs.uiuc.edu/homes/hanj (Ph.D., Univ. of Wisconsin at Madison, 1985), is a professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He has been working on research into data mining, data warehousing, stream data mining, spatial and multimedia data mining, and bio-medical data mining, with over 400 conference and journal publications. He has chaired or served in over 100 program committees of international conferences and workshops, including ACM SIGKDD Conferences (2001 best paper award chair, 2002 student award chair, 1996 PC co-chair), SIAM-Data Mining Conferences (2001 and 2002 PC co-chair), ACM SIGMOD Conferences (2000 exhibit program chair), International Conferences on Data Engineering (2004 and 2002 PC vice-chair), International Conferences on Data Mining (2005 PC co-chair) and International Conference on Very Large Data Bases (2006 VLDB Americas Chair). He also served or is serving as Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data and on the editorial boards for Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Journal of Intelligent Information Systems, and Journal of Computer Science and Technology. Jiawei has received the Outstanding Contribution Award at the 2002 International Conference on Data Mining, ACM Service Award (1999) and ACM SIGKDD Innovations Award (2004), and IEEE CS Technical Achievement Award (2005). He is an ACM and IEEE Fellow. He is the first author of the textbook "Data Mining: Concepts and Techniques" 2nd ed., (Morgan Kaufmann, 2006).

Zhenhui Li is currently a Ph.D. candidate in the Department of Computer Science in the University of Illinois at Urbana-Champaign. She has been working on moving object data mining and has authored and co-authored several research papers related to data warehousing and spatiotemporal data mining. She is currently working on clustering, classification, pattern mining, and outlier detection for moving object data.

Lu An Tang is currently a Ph.D. candidate in the Department of Computer Science in the University of Illinois at Urbana-Champaign. He is currently exploring data mining in traffic data and spatiotemporal data. He is also interested in the analysis of multidimensional data (e.g., OLAP) for traffic data, in the presence of noisy or sampled data.


"Querying Large Graph Databases"   [SLIDES]

Yiping Ke (The Chinese University of Hong Kong, China),
James Cheng (Nanyang Technological University, Singapore),
Jeffrey Xu Yu (The Chinese University of Hong Kong, China)
   
Abstract

Graph exists ubiquitously in a wide spectrum of application domains, such as protein structures in bioinformatics, chemical compounds in chemistry, food webs in ecology, social networks (e.g., Facebook) in social science, Web graphs, P2P networks (e.g., Napster), road networks, XML documents, and many more. With the increasing popularity of graph databases, how to assess graph data effectively and efficiently becomes an important research problem. There have been considerable research efforts for developing advanced query processing techniques on graph databases. This tutorial presents a comprehensive, organized, and state-of-the-art survey on methodologies and techniques on querying large graph databases, including subgraph and supergraph query processing, structural similarity query processing, correlation search in transaction graph databases, connection query processing and approximate matching in large graphs. This tutorial is prepared for database, data mining, and bioinformatics researchers who are interested in complex data types that can be generally modeled as graphs.

Biography

Yiping Ke, www.se.cuhk.edu.hk/~ypke, received her Ph.D. in Computer Science from the Hong Kong University of Science and Technology in 2008 and is currently a research assistant professor in the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong. Before her Ph.D., she received a B.S. in Computer Science from Fudan University, China, in 2003. Her research interests are in database, data mining and information systems, particularly in correlation and association mining, graph indexing, graph query processing and similarity search.

James Cheng, www.ntu.edu.sg/home/jamescheng/, received his Ph.D. degree in Computer Science from the Hong Kong University of Science and Technology in 2008. He is currently an Assistant Professor in the School of Computer Engineering, Nanyang Technological University (NTU), Singapore. His research interests are in the areas of graph databases, social networks, bioinformatics databases, and data streams. His papers are published in prestigious international journals and conferences such as TODS, DMKD, TKDE, SIGMOD, SIGKDD, ICDE, ICDM, EDBT, and SDM.

Jeffrey Xu Yu, www.se.cuhk.edu.hk/~yu, received his B.E., M.E. and Ph.D. in computer science, from the University of Tsukuba, Japan, in 1985, 1987 and 1990, respectively. Dr. Yu held teaching positions in the Institute of Information Sciences and Electronics, University of Tsukuba, Japan, and the Department of Computer Science, The Australian National University. Currently, he is a Professor in the Department of Systems Engineering and Engineering Management, the Chinese University of Hong Kong. Dr. Yu's current main research interest includes graph database, XML database, data mining, Web-technology, and query processing and query optimization. He has published over 190 papers including papers published in TKDE, VLDBJ, TODS, SIGMOD, SIGKDD, VLDB, ICDE, and EDBT. He was an associate editor of IEEE Transactions on Knowledge and Data Engineering, and is a VLDB Journal editorial board member, and a member of ACM SIGMOD Executive Committee.


"Introduction to Social Computing"   [EXT LINK] [SLIDES]

Irwin King (The Chinese University of Hong Kong, China)
Abstract

With the advent of Web 2.0, Social Computing has emerged as one of the hot research topics recently. Social Computing involves the collecting, extracting, accessing, processing, computing, visualizing, etc. of social signals and information. More specifically, this tutorial places special emphases in machine learning, data mining, information retrieval, and other computational techniques involved in collective intelligence processing of social behavior data collected from blogs, wikis, clickthrough data, query logs, tags, etc., and from areas such as social networks, social search, social media, social bookmarks, social news, social knowledge sharing, and social games. In this tutorial, I plan to give an introduction to Social Computing and elaborate on how the various characteristics and aspects are involved in the social platforms for collective intelligence. The topics include social network theory and modeling, graph mining, query log processing, learning to rank, recommender systems, human computation, etc. The tutorial is prepared for machine learning, web mining, and information retrieval researchers who are interested in computational approaches to social computing.

Biography

Irwin King's research interests include machine learning, web intelligence & social computing, and multimedia processing. In these research areas, he has over 200 technical publications in journals (JMLR, ACM TOIS, IEEE TNN, Neurocomputing, NN, IEEE BME, PR, IEEE SMC, JAMC, JASIST, IJPRAI, DSS, etc.) and conferences (NIPS, IJCAI, CIKM, SIGIR, KDD, PAKDD, ICDM, WWW, WI/IAT, WCCI, IJCNN, ICONIP, ICDAR, etc.). In addition, he has contributed over 20 book chapters and edited volumes. Moreover, Dr. King has over 30 research and applied grants. One notable system he has developed is the VeriGuide System, previously known as the CUPIDE (Chinese University Plagiarism IDentification Engine) system, which detects similar sentences and performs readability analysis of text-based documents in both English and in Chinese to promote academic integrity and honesty.

Irwin King is an Associate Editor of the IEEE Transactions on Neural Networks (TNN) and IEEE Computational Intelligence Magazine (CIM). He is a member of the Editorial Board of the Open Information Systems Journal, Journal of Nonlinear Analysis and Applied Mathematics, and Neural Information Processing Letters and Reviews Journal (NIP-LR). He has also served as Special Issue Guest Editor for Neurocomputing, International Journal of Intelligent Computing and Cybernetics (IJICC), Journal of Intelligent Information Systems (JIIS), and International Journal of Computational Intelligent Research (IJCIR). He is a senior member of IEEE and a member of ACM, International Neural Network Society (INNS), and Asian Pacific Neural Network Assembly (APNNA). Currently, he is serving the Neural Network Technical Committee (NNTC) and the Data Mining Technical Committee under the IEEE Computational Intelligence Society (formerly the IEEE Neural Network Society). He is also a Vice-President and Governing Board Member of the Asian Pacific Neural Network Assembly (APNNA).