Efficient Mining of Emerging Events in a Dynamic Spatiotemporal Environment

This paper presents an efficient data mining technique for modeling multidimensional time variant data series and its suitability for mining emerging events in a spatiotemporal environment. The data is modeled using a data structure that interleaves a clustering method with a dynamic Markov chain. Novel operations are used for deleting obsolete states, and finding emerging events based on a scoring scheme. The model is incremental, scalable, adaptive, and suitable for online processing. Algorithm analysis and experiments demonstrate the efficiency and effectiveness of the proposed technique.

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