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