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A Practical Study of Process Mining from Event Logs Using Machine Learning and Petry Net Models

In: Digitalization of Society, Economics and Management

Author

Listed:
  • Valeria Nikitina

    (HSE University)

  • Peter Panfilov

    (HSE University)

Abstract

This practical study is aimed at finding the value of synergy between the process mining and machine learning concepts using python programming. The paper introduces an analysis of an event log data with annual performance results for the purchase process. The purpose was to understand the whole process derived from data, indicate deviations from the standard sequence of events and visualize the process in Petri nets. For this purpose, the input data such as event log is transformed so that the use of process mining open source library is possible. For in-depth analysis the machine learning algorithms such as CatBoost were applied to find out how this sort of data can be used and how the machine learning problem such as regression problem can be solved.

Suggested Citation

  • Valeria Nikitina & Peter Panfilov, 2022. "A Practical Study of Process Mining from Event Logs Using Machine Learning and Petry Net Models," Lecture Notes in Information Systems and Organization, in: Evgeny Zaramenskikh & Alena Fedorova (ed.), Digitalization of Society, Economics and Management, pages 173-185, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-94252-6_13
    DOI: 10.1007/978-3-030-94252-6_13
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