Detecting and monitoring abrupt emergences and submergences of episodes over data streams

Existing studies on episode mining mainly concentrate on the discovery of (global) frequent episodes in sequences. However, frequent episodes are not suited for data streams because they do not capture the dynamic nature of the streams. This paper focuses on detecting dynamic changes in frequencies of episodes over time-evolving streams. We propose an efficient method for the online detection of abrupt emerging episodes and abrupt submerging episodes over streams. Experimental results on synthetic data show that the proposed method can effectively detect the defined patterns and meet the strict requirements of stream processing, such as one-pass, real-time update and return of results, plus limited time and space consumption. Experimental results on real data demonstrate that the patterns detected by our method are natural and meaningful. The proposed method has wide applications in stream monitoring and analysis as the discovered patterns indicate dynamic emergences/disappearances of noteworthy events/phenomena hidden in the streams.

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