Biography

Dr. Matthias Renz
Post-Doctoral Researcher (Assistant Professor) Institute for Computer Science Ludwig-Maximilians Universität München

Dr. Matthias Renz is a Post-Doctoral Teaching and Research Assistant at the Institute for Informatics at the Ludwig-Maximilians University Munich (LMU). He received his Diploma in Electrical Engineering in 1997 from the University of Applied Sciences (Munich) and Diploma in Computer Science in 2002 from LMU. In 2006, he received his PhD degree in Computer Science from the Ludwig-Maximilians University Munich (LMU).
Matthias Renz is a ACM SIGSPATIAL member and serves on the program committees and review panels for leading conferences, journals and workshops including VLDB, SSTD, CIKM, ACM GIS, WISE, ACM TODS, IEEE TKDE, VLDB Journal, Information Systems among others. 2006 he received the Best-Paper Award from the 11. International Conference on Database Systems for Advanced Applications (DASFAA'06). He was general chair of the 1st SIGSPATIAL ACM GIS 2009 International Workshop on Querying and Mining Uncertain Spatio-Temporal Data (QUeST'09). Matthias Renz was invited as keynote speaker for the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data (U'09). He presented tutorials at the
SSTD'09 and DASFAA'09. His research interests include similarity search and mining in uncertain databases, traffic networks, spatial and temporal databases and sensor networks.

Dr. Reynold Cheng
Assistant Professor
Department of Computer Science
University of Hong Kong

Dr. Reynold Cheng is the Assistant Professor of the Department of Computer Science in the University of Hong Kong (HKU). He received his BEng (Computer Engineering) in 1998, and MPhil (Computer Science and Information Systems) in 2000 from HKU. He then obtained his MSc and PhD degrees from the Department of Computer Science in Purdue University, in
2003 and 2005.
Dr. Cheng was the Assistant Professor in the Department of Computing of the Hong Kong Polytechnic University from 2005 to 2008, where he received two Performance Awards. He is a member of IEEE, ACM, ACM SIGMOD, and UPE. He has served on the program committees and review panels for leading database conferences and journals like VLDB, ICDE, and TODS. He is also a guest editor for a special issue in TKDE. He is a keynote speaker in the First International Workshop on Quality of Context (QuaCon '09). His research interests include database management, as well as querying and mining of uncertain data.

Prof. Dr. Hans-Peter Kriegel
Institute for Computer Science
Ludwig-Maximilians Universität München

Hans-Peter Kriegel is a full professor for database systems and data mining in the Department “Institute for Informatics” at the Ludwig-Maximilians-Universität München, Germany and has served as the department chair or vice chair over the last years. His research interests are in spatial and multimedia database systems, particularly in query processing, performance issues, similarity search, high-dimensional indexing as well as in knowledge discovery and data mining. He has published over 300 refereed conference and journal papers and he received the "SIGMOD Best Paper Award" 1997 and the “DASFAA Best Paper Award” 2006 together with members of his research team. In 2009 he was appointed as an ACM fellow for contributions to knowledge discovery and data mining, similarity search, spatial data management, and access methods for high-dimensional data.

Title: Similarity Search and Mining in Uncertain Databases

Managing, searching and mining uncertain data has achieved much attention in the database community recently due to new sensor technologies and new ways of collecting data. There is a number of challenges in terms of collecting, modeling, representing, querying, indexing and mining uncertain data. In its scope, the diversity of approaches addressing these topics is very high because the underlying assumptions of uncertainty are different across different papers. This tutorial provides a comprehensive and comparative overview of general techniques for the key topics in the fields of querying, indexing and mining uncertain data. In particular, it identifies the most generic types of probabilistic similarity queries and discusses general algorithmic methods to answer such queries efficiently. In addition, the tutorial sketches probabilistic methods for important data mining applications in the context of uncertain data with special emphasis on probabilistic clustering and probabilistic pattern mining. The intended audience of this tutorial ranges from novice researchers to advanced experts as well as practitioners from any application domain dealing with uncertain data retrieval and mining.

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