Monitoring Abnormal Patterns with Complex Semantics over ICU Data Streams

  • Xinbiao Zhou
  • Hongyan Li
  • Haibin Liu
  • Meimei Li
  • Lvan Tang
  • Yu Fan
  • Zijing Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4153)


Monitoring abnormal patterns in data streams is an important research area for many applications. In this paper we present a new approach MAPS(Monitoring Abnormal Patterns over data Streams) to model and identify the abnormal patterns over the massive data streams. Compared with other data streams, ICU streaming data have their own features: pseudo-periodicity and polymorphism. MAPS first extracts patterns from the online arriving data streams and then normalizes them according to their pseudo-periodic semantics. Abnormal patterns will be detected if they are satisfied the predicates defined in the clinician-specifying normal patterns. At last, a real application demonstrates that MAPS is efficient and effective in several important aspects.


Data Stream Abnormal Pattern Clinical Information System Complex Semantic Data Stream Management 
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 2006

Authors and Affiliations

  • Xinbiao Zhou
    • 1
  • Hongyan Li
    • 1
  • Haibin Liu
    • 1
  • Meimei Li
    • 1
  • Lvan Tang
    • 1
  • Yu Fan
    • 1
  • Zijing Hu
    • 1
  1. 1.National Laboratory on Machine Perception, School of Electronics Engineering and Computer SciencePeking UniversityP.R. China

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