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Spatio-Temporal Data Mining For Global Scale Eco-Climatic Data

National Science Foundation Award Number: #0713227 (August 1, 2007 - July 31, 2010)

Contact Information:

Vipin Kumar, PI
Department of Computer Science and Engineering
4-192, EE/CSci Building
University of Minnesota
Minneapolis, MN 55455
Phone (612) 625 0726
E-mail: kumar at cs.umn.edu     URL: http://www.cs.umn.edu/~kumar

Michael Steinbach, Co-PI
Department of Computer Science and Engineering
5-225 E, EE/CSci Building
University of Minnesota
Minneapolis, MN 55455
Phone (612) 625-7503
E-mail: steinbach at cs.umn.edu     URL: http://www.cs.umn.edu/~steinbac

List of Supported Students and Staff:


Project Award Information:

Project Summary:

The climate and earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, climate and ecosystem related observations from remote sensors on satellites, as well as outputs of climate or earth system models from large-scale computational platforms, provide terabytes of temporal, spatial and spatio-temporal data. These massive and information-rich datasets offer huge potential for understanding and predicting the behavior of the Earth's ecosystem and for advancing the science of climate change. However, mining patterns from Earth Science data is a difficult task due to the spatio-temporal nature of the data. This project focuses on various challenges involved in the design of algorithms for finding spatio-temporal patterns from such data and their applications in discovering interesting relationships among ecological variables from various parts of the Earth. A special focus is on techniques for land cover change detection (and their use in assessing the impact on carbon cycle) and finding teleconnections between ocean and land variables.

Duration: 3 years.