Remote sensing data from global observing satellites, combined with data from ecosystem models, offers an unprecedented opportunity for predicting and understanding the behavior of the Earth's ecosystem. This data consists of a sequence of global snapshots of the Earth, and includes various atmospheric, land and ocean variables such as sea surface temperature (SST), pressure, precipitation and Net Primary Production (NPP). Due to the large amount of data that is available, data mining techniques are needed to facilitate the automatic extraction and analysis of interesting patterns from the Earth Science data. However, mining patterns from Earth Science data is a difficult task due to the spatio-temporal nature of the data. This project addresses some of the challenges involved in analyzing the data and focuses on the design of efficient algorithms for finding spatio-temporal patterns from such data.
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Mechanical Engineering cover article: Mining what others miss |