Data Intensive vs Sliding Window Outlier Detection in the Stream Data — An Experimental Approach

  • Mateusz Kalisch
  • Marcin Michalak
  • Marek Sikora
  • Łukasz Wróbel
  • Piotr Przystałka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9693)


In the paper a problem of outlier detection in the stream data is raised. The authors propose a new approach, using well known outlier detection algorithms, of outlier detection in the stream data. The method is based on the definition of a sliding window, which means a sequence of stream data observations from the past that are closest to the newly coming object. As it may be expected the outlier detection accuracy level of this model becomes worse than the accuracy of the model that uses all historical data, but from the statistical point of view the difference is not significant. In the paper several well known methods of outlier detection are used as the basis of the model.


Outlier detection Data analysis Classification Time series 



This work was partially supported by Polish National Centre for Research and Development (NCBiR) grant PBS2/B9/20/2013 within Applied Research Programmes. The infrastructure was supported by “PL-LAB2020” project, contract POIG.02.03.01-00-104/13-00.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mateusz Kalisch
    • 1
  • Marcin Michalak
    • 2
  • Marek Sikora
    • 2
    • 3
  • Łukasz Wróbel
    • 3
  • Piotr Przystałka
    • 1
  1. 1.Institute of Fundamentals of Machinery DesignSilesian University of TechnologyGliwicePoland
  2. 2.Institute of InformaticsSilesian University of TechnologyGliwicePoland
  3. 3.Institute of Innovative Technologies EMAGKatowicePoland

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