Detecting abnormal trend evolution over multiple data streams

  • Chen Zhang*
  • , Nianlong Weng
  • , Jianlong Chang
  • , Aoying Zhou
  • *Corresponding author for this work

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

12 Scopus citations

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.

Original languageEnglish
Title of host publicationAdvances in Data and Web Management - Joint International Conferences, APWeb/WAIM 2009, Proceedings
PublisherSpringer Verlag
Pages285-296
Number of pages12
ISBN (Print)9783642006715
DOIs
StatePublished - 2009
EventJoint International Conference on Advances in Data and Web Management, APWeb/WAIM 2009 - Suzhou, China
Duration: 2 Apr 20094 Apr 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5446
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceJoint International Conference on Advances in Data and Web Management, APWeb/WAIM 2009
Country/TerritoryChina
CitySuzhou
Period2/04/094/04/09

Keywords

  • Anomaly detection
  • Data stream trend analysis

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