Elastic Burst Detection

  • Dennis Shasha
  • Yunyue Zhu
Part of the Monographs in Computer Science book series (MCS)

Summary

Burst detection is the activity of rinding 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 and system called OmniBurst [104] for detecting interesting aggregates over such elastic windows in near linear time. We present applications of the algorithm to 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 filters out periods of non-bursts in linear time, so beats the quadratic direct computation approach (of testing all window sizes individually) by several orders of magnitude.

Keywords

Window Size Data Stream Binary Tree Detailed Search Window Model 
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

© Dennis E. Shasha and Yunyue Zhu 2004

Authors and Affiliations

  • Dennis Shasha
    • 1
  • Yunyue Zhu
    • 1
  1. 1.Courant InstituteNew YorkUSA

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