Held in conjunction with the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Important Dates

• Paper Submission Due:
• September 4, 2009
• Notification to the authors:
• September 28, 2009
• Camera ready papers due:
• October 8, 2009
• ACM GIS 2009 Conference:
• November 4-6, 2009
• QUeST Workshop:
• November 3, 2009

Organization

• General Chairs
• Matthias Renz
• Peer Kröger
• Web Site Chair
• Thomas Bernecker

Keynote Speakers:

Jian Pei, Simon Fraser University, Canada

Title:

Dealing with Uncertainty in Spatial Data: Instances, Objects, Possible Worlds, and Probability Distributions

Uncertainty is inherent and ubiquitous in many spatial applications. In this talk, I will present our recent explorations on analyzing large uncertain data sets on several interesting aspects. First, to understand and summarize an uncertain object, I will illustrate a method to pick the tricky. Last, I will exemplify the pros and cons of using the recently prevailing possible world model and the traditional probability distribution similarity in analyzing uncertain spatial data.

Jian Pei is currently an Associate Professor and the Associate Director (Research and Industry Relations) at the School of Computing Science, Simon Fraser University, Canada. He is interested in developing effective and efficient data analysis techniques for novel data intensive applications, including data mining, data warehousing, online analytical processing, database systems, and information retrieval, as well as their applications in web search, social networks, health-informatics, bioinformatics and business intelligence. His research has been well supported by numerous government funding agencies and industry partners. He has published prolifically and served actively in the research community. He is the recipients of several prestigious awards and honors.

Mohamed F. Mokbel, University of Minnesota, Minneapolis, MN, USA

Title:

Spatial and Spatio-temporal Data Uncertainty: Modeling and Querying

Whether it is inherent in location-detection device inaccurate reading or embedded in accurate location data as a means of achieving privacy, data uncertainty becomes ubiquitous in many spatial and spatio-temproal applications. Examples include sensor networks, location-based services, and geographic information systems. In this talk, we will focus on two main aspects of data uncertainty, namely modeling and querying. In the modeling part, we will discuss various ways of modeling data uncertainly for spatial and spatio-temporal data. In the querying part, we will discuss how to tune existing query processors to tolerate data uncertainty. Throughout the talk, we will use preference queries and privacy in location-based services as our driving applications.

Mohamed Mokbel is an assistant professor in the Department of Computer Science and Engineering, University of Minnesota. His main research interests focus on advancing the state of the art in the design and implementation of database engines to cope with the requirements of emerging applications (e.g., location-based applications and sensor networks). Mohamed has been the main architect for the Casper system that is considered as the first attempt to provide a database support for privacy-preserving location-based queries. His most recent research includes embedding context and preference-awareness inside database management systems. His research work has been recognized by two best paper awards at IEEE MASS 2008 and MDM 2009. Mohamed has been a visiting scholar/researcher at Hong Kong Polytechnic University and Microsoft Research for Summers 2006 and 2008, respectively. Mohamed has actively participated in several program committees for major conferences including ICDE, SIGMOD, VLDB, SSTD, and ACM GIS. He is/was a program co-chair for ACM SIGSPATIAL GIS 2008 and 2009. Mohamed is an ACM and IEEE member and a founding member for ACM SIGSPATIAL. For more information, please visit http://www.cs.umn.edu/~mokbel.