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Spatio-Temporal Data Mining to Survey Global Ocean Dynamics

Monday, November 18, 2013 - 9:45am - 10:25am
Lind 305
James Faghmous (University of Minnesota, Twin Cities)
Our planet is experiencing simultaneous changes in global population, urbanization, and climate. These changes, along with the rapid growth of climate data and increasing popularity of data mining techniques may lead to the conclusion that the time is ripe for data mining to spur major innovations in climate science. However, climate data bring forth unique challenges that are unfamiliar to the traditional data mining literature, and unless they are addressed, data mining will not have the same impact that it has had on fields such as biology or e-commerce.

This talk provides a technical audience with an introduction to mining climate data with an emphasis on the singular characteristics of the datasets and research questions climate science attempts to address. We demonstrate some of the concepts discussed in the earlier parts of the talk with a spatio-temporal pattern mining application to monitor global ocean eddy dynamics. We show that insightfully mining the spatio-temporal context of climate datasets can yield significant improvements in the performance of learning algorithms.
MSC Code: 
97R50
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