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Portuguese Conference on Artificial Intelligence

EPIA 2015: Progress in Artificial Intelligence pp 547–559Cite as

Periodic Episode Discovery Over Event Streams

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9273))

Abstract

Periodic behaviors are an important component of the life of most living species. Daily, weekly, or even yearly patterns are observed in both human and animal behaviors. These behaviors are searched as frequent periodic episodes in event streams. We propose an efficient algorithm for the discovery of frequent and periodic episodes. Update procedures allow us to take into account that behaviors also change with time, or because of external factors. The interest of our approach is illustrated on two real datasets.

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Author information

Authors and Affiliations

  1. UMR 6285 Lab-STICC, Institut Mines-Telecom, Telecom Bretagne, Technopôle Brest Iroise CS 83818, 29238, Brest Cedex 3, France

    Julie Soulas & Philippe Lenca

  2. Université Européenne de Bretagne, Rennes, France

    Julie Soulas & Philippe Lenca

Authors
  1. Julie Soulas
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  2. Philippe Lenca
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Corresponding author

Correspondence to Julie Soulas .

Editor information

Editors and Affiliations

  1. ISEC - Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Coimbra, Portugal

    Francisco Pereira

  2. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Ernesto Costa

  4. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Amílcar Cardoso

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© 2015 Springer International Publishing Switzerland

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Soulas, J., Lenca, P. (2015). Periodic Episode Discovery Over Event Streams. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_54

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  • DOI: https://doi.org/10.1007/978-3-319-23485-4_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23484-7

  • Online ISBN: 978-3-319-23485-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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