Efficient elastic burst detection in data streams

Yunyue Zhu, Dennis Shasha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Burst detection is the activity of finding abnormal aggregates in data streams. Such aggregates are based on sliding windows over data streams. In some applications, we want to monitor many sliding window sizes simultaneously and to report those windows with aggregates significantly different from other periods. We will present a general data structure for detecting interesting aggregates over such elastic windows in near linear time. We present applications of the algorithm for detecting Gamma Ray Bursts in large-scale astrophysical data. Detection of periods with high volumes of trading activities and high stock price volatility is also demonstrated using real time Trade and Quote (TAQ) data from the New York Stock Exchange (NYSE). Our algorithm beats the direct computation approach by several orders of magnitude.

Original languageEnglish (US)
Title of host publicationProceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
Pages336-345
Number of pages10
DOIs
StatePublished - 2003
Event9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 - Washington, DC, United States
Duration: Aug 24 2003Aug 27 2003

Other

Other9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
CountryUnited States
CityWashington, DC
Period8/24/038/27/03

Keywords

  • Data stream
  • Elastic burst

ASJC Scopus subject areas

  • Software
  • Information Systems

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