2008 ConstructingComprehensiveSummar

• (Kiernan et al., 2008) ⇒ Jerry Kiernan, and Evimaria Terzi. (2008). “Constructing Comprehensive Summaries of Large Event Sequences.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401943

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• http://scholar.google.com/scholar?q=%22Constructing+comprehensive+summaries+of+large+event+sequences%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401943&preflayout=flat#citedby

Quotes

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ConstructingComprehensiveSummar	Evimaria Terzi Jerry Kiernan			Constructing Comprehensive Summaries of Large Event Sequences				10.1145/1401890.1401943