1st Workshop on Spatial and Spatio-temporal Data Mining (SSTDM)

In conjunction with ICDM 2006

December 18, 2006, Hong Kong

The recent advances of telecommunications (e.g., GPS, Cellular networks, etc.) has facilitated the collection of large spatial and spatio-temporal datasets. The volume of such data and their potentially high update rate makes their manual analysis extremely difficult (if not impossible), calling for mining techniques for automatic extraction of valuable information. Furthermore the special nature of the data and the analysis objectives renders knowledge extraction techniques for simple data types inadequate. Special issues in this data mining field include the fuzzy and implicit nature of spatial and spatio-temporal relationships between objects, the complex geometry of spatial objects, the varying temporal nature of events (instantaneous vs. durable), the variability of spatio-temporal data (moving objects, evolution of spatial events or phenomena, etc.), and the multiple (spatial and temporal) resolution levels of abstraction.

This workshop aims at bringing together researchers and practitioners of spatial and spatio-temporal data mining. Topics that will be addressed in the workshop include but are not limited to:

 Algorithmic frameworks for spatial/spatio-temporal data mining

 Novel spatial and spatio-temporal data mining tasks

 Knowledge representation, handling uncertainty and fuzziness in spatial and spatio-temporal data mining

 Visualization of spatial/spatio-temporal data mining results

 Mining spatial data at different resolution and abstraction levels

 Mining spatial and spatio-temporal patterns in web data

 Spatial and spatio-temporal mining for streaming and sensor network data

 Integration to non-spatial data mining engines or spatial database systems

 Analyzing data on transportation networks

 Distributed and parallel spatial data mining

 Mining census and GIS data

 Mining meteorological data

 Spatial data mining for medical and biological data

 Case Studies and applications of spatial and spatio-temporal data mining