SSTDM 2009 : International Workshop on Spatial and Spatiotemporal Data Mining
Call For Papers
International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-09)
In Cooperation with IEEE ICDM 2009, 6 December 2009, Miami, Florida, USA.
July 15, 2009
September 8, 2009
September 28, 2009
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
Climate Change, Natural Hazards, Critical Infrastructures
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 2009 standard guidelines, will be considered for this workshop. Papers should be submitted using the ICDM workshop paper submission form (more details will be posted on the workshop 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.
Shashi Shekar, University of Minnesota
Peggy Agouris, George Mason University
Government, Industry and Publicity Chairs
Budhendra Bhaduri, Oak Ridge National Laboratory
Milind Deshpande, IBM T.J. Watson Research Lab
Hui Xiong, Rutgers University
Ranga Raju Vatsavai, Oak Ridge National Laboratory
Guido Cervone, George Mason University
Jin Soung Yoo, Indiana University-Purdue University
Jessica Lin, George Mason University
Chiara Renso, KDDLAB - ISTI CNR