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Constructing comprehensive summaries of large event sequences

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 appearing in a sequence. While interesting, these patterns do not give a comprehensive summary of the entire event sequence. Moreover, the number of patterns discovered can be large. In this article, we take an alternative approach and build short summaries that describe an entire sequence, and discover local dependencies between event types. We formally define the summarization problem as an optimization problem that balances shortness of the summary with 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Constructing comprehensive summaries of large event sequences

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References (44)

Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
Subject
Algorithms
ISSN
1556-4681
DOI
10.1145/1631162.1631169
Publisher site
See Article on Publisher Site

Abstract

Event sequences capture system and user activity over time. Prior research on sequence mining has mostly focused on discovering local patterns appearing in a sequence. While interesting, these patterns do not give a comprehensive summary of the entire event sequence. Moreover, the number of patterns discovered can be large. In this article, we take an alternative approach and build short summaries that describe an entire sequence, and discover local dependencies between event types. We formally define the summarization problem as an optimization problem that balances shortness of the summary with 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.

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery

Published: Nov 1, 2009

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