Synopsis: With advances in remote sensors, sensor networks, and the proliferation of location sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatiotemporal data has exploded in recent years. In addition, significant progress in ground, air- and space-borne sensor technologies has led to an unprecedented access to earth science data for scientists from different disciplines, interested in studying the complementary nature of different parameters. These developments are quickly leading towards a data-rich but information-poor environment. The rate at which geospatial data are being generated clearly exceeds our ability to organize and analyze them to extract patterns critical for understanding in a timely manner a dynamically changing world. Computer science and geoinformatics are collaborating in order to address these scientific and computational challenges and provide innovative and effective solutions.

More specifically, efficient and reliable data mining techniques are needed for extracting useful geoinformation from large heterogeneous, often multi-modal spatiotemporal datasets. Traditional data mining techniques are ineffective as they do not incorporate the idiosyncrasies of the spatial domain, which include (but are not limited to) spatial autocorrelation, spatial context, and spatial constraints. Extracting useful geoinformation from several terabytes of streaming multi-modal data per day also demands the use of modern computing in all its forms. Thus, we invite computer science and geoinformatics researchers to participate in this event in order to share, contribute, and discuss the emerging challenges in spatial and spatiotemporal data mining.

Topics: The major topics of interest to the workshop include but are not limited to:

* Theoretical foundations of spatial and spatiotemporal data mining
* Spatial and spatiotemporal analogues of interesting patterns: frequent itemsets, clusters,outliers, and the algorithms to mine them
* Spatial classification: methods that explicitly model spatial context
* Spatial and spatiotemporal autocorrelation and heterogeneity, its quantification and
efficient incorporation into the data mining algorithms
* Image (multispectral, hyperspectral, aerial, radar) information mining, change detection
* Role of uncertainty in spatial and spatiotemporal data mining
* Integrated approaches to multi-source and multimodal data mining
* Resource-aware techniques to mine streaming spatiotemporal data
* Spatial and spatiotemporal data mining at multiple granularities (space and time)
* Data structures and indexing methods for spatiotemporal data mining
* Spatial and Spatiotemporal online analytical processing, data warehousing
* Geospatial Intelligence
* Climate Change, Natural Hazards, Critical Infrastructures
* High-performance SSTDM
* Applications that demonstrate success stories of spatial and spatiotemporal data mining

Paper Submission: This is an open call-for-papers. Only original and high-quality papers conforming to the ICDM 2010 standard guidelines (8 pages) will be considered for this workshop. Detailed submission instructions will be posted on SSTDM-10 (http://www.ornl.gov/sci/knowledgediscovery/sstdm-10/) website.

Proceedings: Accepted papers will be included in a ICDM Workshop Proceedings volume, to be published by IEEE Computer Society Press, which will also be included in the IEEE Digital Library.

Organizing Committee:

General Chairs
Shashi Shekar, University of Minnesota, USA
Peggy Agouris, George Mason University, USA

Program Chairs
Ranga Raju Vatsavai, Oak Ridge National Laboratory, USA
Sanjay Chawla, University of Sydney, Australia
Matt Duckham, University of Melbourne, Australia

Government, Industry and Publicity Chairs
Budhendra Bhaduri, Oak Ridge National Laboratory, USA
Jing (David) Dai, IBM T. J. Watson Research Lab, USA
Varun Chandola, Oak Ridge National Laboratory, USA

Program Committee: (To be confirmed soon)