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Application of a Genetic Algorithm for Finding Edit Distances between Process Models

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Abstract—

Finding graph edit distances (determining the similarity of graph models) is an important task in various areas of computer science, such as image analysis, machine learning, and chemoinformatics. In recent years, due to the development of process mining techniques, it has become necessary to adapt the existing graph matching methods to be applied to the analysis of process models (annotated graphs) discovered from event logs of information systems. In particular, methods for finding the minimum graph edit distance can be used to reveal patterns (subprocesses) and to compare discovered process models. As was shown experimentally and theoretically substantiated, exact methods for finding the minimum edit distance between the discovered process models (and graphs in the general case) have a great time complexity and can be applied only to small-sized process models. In this paper, we estimate the accuracy and time performance characteristics of a genetic algorithm applied to find distances between process models discovered from the event logs. In particular, we find distances between BPMN (Business Process Model and Notation) models discovered from the event logs by using different synthesis algorithms. It is shown that the genetic algorithm proposed in the paper allows us to significantly reduce the computation time and produces results close to the optimal solutions (the minimum edit distances).

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  1. http://www.processmining.org.

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Funding

The research was supported by the Grant of the President of the Russian Federation for young Russian scientists — Candidates of Sciences no. MK-4188.2018.9.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Laboratory of Process-Aware Information Systems, 20 Myasnitskaya St., 101000, Moscow, Russia

    A. A. Kalenkova

  2. National Research University Higher School of Economics, Faculty of Computer Science, 20 Myasnitskaya St., 101000, Moscow, Russia

    D. A. Kolesnikov

Authors
  1. A. A. Kalenkova
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  2. D. A. Kolesnikov
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Corresponding authors

Correspondence to A. A. Kalenkova or D. A. Kolesnikov.

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The authors declare that they have no conflicts of interest.

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Translated by I. Tselishcheva

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Kalenkova, A.A., Kolesnikov, D.A. Application of a Genetic Algorithm for Finding Edit Distances between Process Models. Aut. Control Comp. Sci. 53, 617–627 (2019). https://doi.org/10.3103/S0146411619070071

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  • DOI: https://doi.org/10.3103/S0146411619070071

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