An algorithmic approach to event summarization

Peng Wang, Haixun Wang, Majin Liu, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

41 Scopus citations


Recently, much study has been directed toward summarizing event data, in the hope that the summary will lead us to a better understanding of the system that generates the events. However, instead of offering a global picture of the system, the summary obtained by most current approaches are piecewise, each describing an isolated snapshot of the system. We argue that the best summary, both in terms of its minimal description length and its interpretability, is the one obtained with the understanding of the internal dynamics of the system. Such understanding includes, for example, what are the internal states of the system, and how the system alternates among these states. In this paper, we adopt an algorithmic approach for event data summarization. More specifically, we use a hidden Markov model to describe the event generation process. We show that summarizing events based on the learned hidden Markov Model achieves short description length and high interpretability. Experiments show that our approach is both efficient and effective.

Original languageEnglish
Title of host publicationProceedings of the 2010 International Conference on Management of Data, SIGMOD '10
Number of pages12
StatePublished - 2010
Externally publishedYes
Event2010 International Conference on Management of Data, SIGMOD '10 - Indianapolis, IN, United States
Duration: 6 Jun 201011 Jun 2010

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078


Conference2010 International Conference on Management of Data, SIGMOD '10
Country/TerritoryUnited States
CityIndianapolis, IN


  • event summarization
  • hidden markov model
  • minimal description length


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