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International Conference on Computational Collective Intelligence

ICCCI 2016: Computational Collective Intelligence pp 249–259Cite as

On Systematic Approach to Discovering Periodic Patterns in Event Logs

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9875))

Abstract

Discovering periodic patterns from historical information is a computationally hard problem due to the large amounts of historical data to be analyzed and due to a high complexity of the patterns. This work shows how the derivations rules for periodic patterns can be applied to discover complex patterns in case of logs of events. The paper defines a concept of periodic pattern and its validation in a workload trace created from the logs of events. A system of derivations rules that transforms periodic patterns into the logically equivalent ones is proposed. The paper presents a systematic approach based on the system of derivation rules to discovery of periodic patterns in logs of events.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, TU Chemnitz, Chemnitz, Germany

    Marcin Zimniak

  2. School of Computer Science and Software Engineering, University of Wollongong, Wollongong, Australia

    Janusz R. Getta

Authors
  1. Marcin Zimniak
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  2. Janusz R. Getta
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Corresponding authors

Correspondence to Marcin Zimniak or Janusz R. Getta .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc-Thanh Nguyen

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Lazaros Iliadis

  3. Department of Forestry and Management, Democritus University of Thrace, Orestiada, Thrace, Greece

    Yannis Manolopoulos

  4. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

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© 2016 Springer International Publishing Switzerland

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Zimniak, M., Getta, J.R. (2016). On Systematic Approach to Discovering Periodic Patterns in Event Logs. In: Nguyen, NT., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9875. Springer, Cham. https://doi.org/10.1007/978-3-319-45243-2_23

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  • DOI: https://doi.org/10.1007/978-3-319-45243-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45242-5

  • Online ISBN: 978-3-319-45243-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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