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Frequent episode discovery is a popular framework for pattern discovery inevent streams. An episode is a partially ordered set of nodes with each nodeassociated with an event type. Efficient (and separate) algorithms exist forepisode discovery when the associated partial order is total (serial episode)and trivial (parallel episode). In this paper, we propose efficient algorithmsfor discovering frequent episodes with general partial orders. These algorithmscan be easily specialized to discover serial or parallel episodes. Also, thealgorithms are flexible enough to be specialized for mining in the space ofcertain interesting subclasses of partial orders. We point out that there is aninherent combinatorial explosion in frequent partial order mining and mostimportantly, frequency alone is not a sufficient measure of interestingness. Wepropose a new interestingness measure for general partial order episodes and adiscovery method based on this measure, for filtering out uninteresting partialorders. Simulations demonstrate the effectiveness of our algorithms.

Discovering general partial orders in event streams