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Mining Periodic Patterns with a MDL Criterion

  • Esther GalbrunEmail author
  • Peggy Cellier
  • Nikolaj Tatti
  • Alexandre Termier
  • Bruno Crémilleux
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11052)

Abstract

The quantity of event logs available is increasing rapidly, be they produced by industrial processes, computing systems, or life tracking, for instance. It is thus important to design effective ways to uncover the information they contain. Because event logs often record repetitive phenomena, mining periodic patterns is especially relevant when considering such data. Indeed, capturing such regularities is instrumental in providing condensed representations of the event sequences.

We present an approach for mining periodic patterns from event logs while relying on a Minimum Description Length (MDL) criterion to evaluate candidate patterns. Our goal is to extract a set of patterns that suitably characterises the periodic structure present in the data. We evaluate the interest of our approach on several real-world event log datasets. Code related to this paper is available at: https://github.com/nurblageij/periodic-patterns-mdl.

Keywords

Periodic patterns MDL Sequence mining 

Notes

Acknowledgements

The authors thank Hiroki Arimura and Jilles Vreeken for valuable discussions. This work has been supported by Grenoble Alpes Metropole through the Nano2017 Itrami project, by the QCM-BioChem project (CNRS Mastodons) and by the Academy of Finland projects “Nestor” (286211) and “Agra” (313927).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Esther Galbrun
    • 1
    Email author
  • Peggy Cellier
    • 2
  • Nikolaj Tatti
    • 1
    • 4
  • Alexandre Termier
    • 2
  • Bruno Crémilleux
    • 3
  1. 1.Department of Computer ScienceAalto UniversityEspooFinland
  2. 2.Univ. Rennes, {INSA, Inria}, CNRS, IRISARennesFrance
  3. 3.Normandie Univ., UNICAEN, ENSICAEN, CNRS – UMR GREYCCaenFrance
  4. 4.F-SecureHelsinkiFinland

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