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Data mining in large spatiotemporal data sets Dr Amy McGovern [email protected] Associate Professor, School of Computer Science Adjunct Associate Professor, School of Meteorology

Data mining in large spatiotemporal data sets Dr Amy McGovern [email protected] Associate Professor, School of Computer Science Adjunct Associate Professor,

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Page 1: Data mining in large spatiotemporal data sets Dr Amy McGovern amcgovern@ou.edu Associate Professor, School of Computer Science Adjunct Associate Professor,

Data mining in large spatiotemporal data sets

Dr Amy [email protected]

Associate Professor, School of Computer ScienceAdjunct Associate Professor, School of Meteorology

Page 2: Data mining in large spatiotemporal data sets Dr Amy McGovern amcgovern@ou.edu Associate Professor, School of Computer Science Adjunct Associate Professor,

Overview

• My lab develops and applies spatiotemporal relational data mining methods for large data sets

• Example data sets:– Severe weather prediction

including tornadoes, hail, severe wind events

– Aircraft turbulence prediction

Page 3: Data mining in large spatiotemporal data sets Dr Amy McGovern amcgovern@ou.edu Associate Professor, School of Computer Science Adjunct Associate Professor,

Relevance

• Goals: – Automatic discovery of spatial, temporal, and

spatiotemporal relationships that can predict events– Enable domain scientists to revolutionize their

understanding of the causes of the event• Large data sets cannot be understood or mined by

hand– Automated methods must be used– Our methods aim for knowledge discovery not just

data mining

Page 4: Data mining in large spatiotemporal data sets Dr Amy McGovern amcgovern@ou.edu Associate Professor, School of Computer Science Adjunct Associate Professor,

Collaboration interests

• Our methods are general and will apply beyond severe weather. We are interested in new collaborators!

• Interested in large and complex data sets with spatial, temporal, or spatiotemporal components– Prefer prediction tasks– Prefer non-text data