Discovering Infrequent Behavioral Patterns in Process Models

  • David Chapela-Campa
  • Manuel Mucientes
  • Manuel Lama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10445)

Abstract

Process mining has focused, among others, on the discovery of frequent behavior with the aim to understand what is mainly happening in a process. Little work has been done involving uncommon behavior, and mostly centered on the detection of anomalies or deviations. But infrequent behavior can be also important for the management of a process, as it can reveal, for instance, an uncommon wrong realization of a part of the process. In this paper, we present WoMine-i, a novel algorithm to retrieve infrequent behavioral patterns from a process model. Our approach searches in a process model extracting structures with sequences, selections, parallels and loops, which are infrequently executed in the logs. This proposal has been validated with a set of synthetic and real process models, and compared with state of the art techniques. Experiments show that WoMine-i can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.

Keywords

Infrequent patterns Process mining Process discovery 

Notes

Acknowledgments

This research was supported by the Spanish Ministry of Economy and Competitiveness (grant TIN2014-56633-C3-1-R) and the Galician Ministry of Education, Culture and Universities (grants GRC2014/030 and accreditation 2016-2019, ED431G/08). These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • David Chapela-Campa
    • 1
  • Manuel Mucientes
    • 1
  • Manuel Lama
    • 1
  1. 1.Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS)Universidade de Santiago de CompostelaSantiago de CompostelaSpain

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