Constructing Comprehensive Summaries of Large Event Sequences

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Executive Summary

Event sequences capture system and user activity over time. Prior research on sequence mining has mostly focused on discovering local patterns. Though interesting, these pat-terns reveal local associations and fail to give a comprehensive summary of the entire event sequence. Moreover, the number of patterns discovered can be large. This paper takes an alternative approach and builds short summaries that describe the entire sequence, while revealing local associations among events. The paper formally defines the summarization problem as an optimization problem that balances between shortness of the summary and accuracy of the data description.

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