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

Valeria Nikitina and Peter Panfilov ()
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Valeria Nikitina: HSE University
Peter Panfilov: HSE University

A chapter in Digitalization of Society, Economics and Management, 2022, pp 173-185 from Springer

Abstract: 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.

Keywords: Business process intelligence; Machine learning; Data mining; Process mining; Petry net (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-94252-6_13

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DOI: 10.1007/978-3-030-94252-6_13

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