document: Constructing comprehensive summaries of large event sequences Event sequences capture system and user activity over time . Prior research on sequence mining has mostly focused on discovering local patterns . Though interesting , these patterns reveal local associations and fail to give a comprehensive summary of the entire event sequence . Moreover , the number of patterns discovered can be large . In this paper , we take an alternative approach and build short summaries that describe the entire sequence , while revealing local associations among events . We formally define the summarization problem as an optimization problem that balances between shortness of the summary and accuracy of the data description . We show that this problem can be solved optimally in polynomial time by using a combination of two dynamic-programming algorithms . We also explore more efficient greedy alternatives and demonstrate that they work well on large datasets . Experiments on both synthetic and real datasets illustrate that our algorithms are efficient and produce high-quality results , and reveal interesting local structures in the data .
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