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A Latitudinal Study on the Use of Sequential and Concurrency Patterns in Deviance Mining

  • Laura GengaEmail author
  • Domenico Potena
  • Andrea Chiorrini
  • Claudia Diamantini
  • Nicola Zannone
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 880)

Abstract

Deviance mining is an emerging area in the field of Process Mining, with the aim of explaining the differences between normal and deviant process executions. Deviance mining approaches typically extract representative subprocesses characterizing normal/deviant behaviors from an event log and use these subprocesses as features for classification. Existing approaches mainly differ for the employed feature extraction technique and, in particular, for the representation of the patterns extracted, ranging from patterns consisting of sequence of activities to patterns explicitly representing concurrency. In this work, we perform a latitudinal study on the use of sequential and concurrency patterns in deviance mining. Comparisons between sequential and concurrency patterns is performed through experiments on two real-world event logs, by varying both classification and feature extraction algorithms. Our results show that the pattern representation has limited impact on classification performance, while the use of concurrency patterns provides more meaningful insights on deviant behavior.

Notes

Acknowledgements

This work is partially supported by ITEA3 through the APPSTACLE project (15017) and by the RSA-B project SeCludE.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Laura Genga
    • 1
    Email author
  • Domenico Potena
    • 2
  • Andrea Chiorrini
    • 2
  • Claudia Diamantini
    • 2
  • Nicola Zannone
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Università Politecnica delle MarcheAnconaItaly

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