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Effective Similarity Analysis over Event Streams Based on Sharing Extent

  • Yanqiu Wang
  • Ge Yu
  • Tiancheng Zhang
  • Dejun Yue
  • Yu Gu
  • Xiaolong Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5446)

Abstract

With the development of event-driven applications, event stream processing has received more and more attentions in database community. However, little work has focused on the problem of data mining and similarity analysis among event streams. As the foundation for the data mining such as frequent or abnormal event pattern detection, efficient similarity search is desired to be first executed. In this paper, we attempt to take the first step into the similarity search in the context of vast event streams. We propose a simple but effective model to improve the efficiency of the similarity search. To avoid redundant pair-wise comparison, we adopt the definition of sharing extent to dramatically filter dissimilar event streams and speed up the calculation of similarity. Extensive simulated experiments have demonstrated that our model and algorithm can lead to higher efficiency when guaranteeing expected accuracy.

Keywords

Execution Time Event Type Edit Distance Event Stream Slide Window Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yanqiu Wang
    • 1
  • Ge Yu
    • 1
  • Tiancheng Zhang
    • 1
  • Dejun Yue
    • 1
  • Yu Gu
    • 1
  • Xiaolong Hu
    • 1
  1. 1.School of Information Science and EngineeringNortheastern UniversityChina

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