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Detecting Abnormal Trend Evolution over Multiple Data Streams

  • Chen Zhang
  • Nianlong Weng
  • Jianlong Chang
  • Aoying Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5446)

Abstract

In this paper, we present a method to trace evolution of trend over multiple data streams and detect the abnormal ones. First of all, a definition of trend for single data stream is provided, the advantage of our definition lies in its low time and space cost. Second, we improve a SVD-based method in order to select a pair of optimal initial parameters, then a novel chessboard named sketch is also illustrated aim at adjusting the parameters dynamically. Then, utilizing the skewness of trend distribution, an anomaly detection strategy is briefly introduced. Finally, we implement experiment on a variety of real data sets to illustrate effectiveness and efficiency of our approach.

Keywords

Data Stream Trend Analysis Anomaly Detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chen Zhang
    • 1
  • Nianlong Weng
    • 1
    • 2
  • Jianlong Chang
    • 1
    • 3
  • Aoying Zhou
    • 4
    • 5
  1. 1.Department of Computer Science and EngineeringFudan UniversityP.R. China
  2. 2.ShangHai Stock ExchangeP.R. China
  3. 3.ShangHai Telecom CompanyP.R. China
  4. 4.Software Engineering Institute of East China Normal UniversityP.R. China
  5. 5.Shanghai Key Laboratory of Trustworthy ComputeringP.R. China

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