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

2. 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

3. 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

4. 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