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Comparative Analysis of Process Mining Algorithms in Python

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Smart Objects and Technologies for Social Good (GOODTECHS 2021)

Abstract

In many sectors, there is a large amount of data collected and stored, which is not analyzed. The health area is a good example. This situation is not desirable, as the data can provide historical information or trends that may help to improve organizations performance in the future. Process mining allows the extraction of knowledge from data generated and stored in the information systems.

This work aims to contribute to the aforementioned knowledge extraction, comparing different algorithms in process mining techniques, using health care processes and data. The results showed that Inductive Miner and Heuristic Miner are the algorithms with better results. Considering the execution times, Petri Net is the type of model that takes longer, but it is the one that allows a better analysis.

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Notes

  1. 1.

    Noise is the result of data quality problems, such as registration errors, which infrequently manifest themselves in the behavior of the process [13].

  2. 2.

    A Petri net has two types of elements, positions and transitions. A position can contain one or more tokens. A transition is enabled if all inputs (positions connected to itself) contain, at least, one token [14].

  3. 3.

    Process tree is a tree-structured process model, where leaf nodes represent activities, and non-leaf nodes represent control flow operators [28].

  4. 4.

    Transitional system is used to describe the potential behavior of discrete systems. It consists of states and transitions between states [29].

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Acknowledgements

This work is funded by National Funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project Ref UIDB/05583/2020. Furthermore, we would like to thank the Research Centre in Digital Services (CISeD), the Polytechnic of Viseu for their support.

This work is also funded by National Funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project Refª UIDB/05507/2020. Furthermore we would like to thank the Centre for Studies in Education and Innovation (CI&DEI) and the Polytechnic of Viseu for their support.

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Authors and Affiliations

  1. Polytechnic Institute of Viseu, Viseu, Portugal

    André Filipe Domingos Gomes

  2. Escola Superior Tecnologia e Gestão de Viseu, CISeD, Polytechnic Institute of Viseu, Viseu, Portugal

    Ana Cristina Wanzeller Guedes de Lacerda

  3. Escola Superior Tecnologia e Gestão de Viseu, CI&DEI, Polytechnic Institute of Viseu, Viseu, Portugal

    Joana Rita da Silva Fialho

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  1. André Filipe Domingos Gomes
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  2. Ana Cristina Wanzeller Guedes de Lacerda
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  3. Joana Rita da Silva Fialho
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Correspondence to Joana Rita da Silva Fialho .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal

    Ivan Miguel Pires

  2. Marche Polytechnic University, Ancona, Italy

    Susanna Spinsante

  3. Faculty of Computer Science and Engineering, Sts Cyril and Methodius University, Skopje, North Macedonia

    Eftim Zdravevski

  4. Faculty of Computer Science and Engineering, Sts Cyril and Methodius University, Skopje, North Macedonia

    Petre Lameski

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Gomes, A.F.D., de Lacerda, A.C.W.G., da Silva Fialho, J.R. (2021). Comparative Analysis of Process Mining Algorithms in Python. In: Pires, I.M., Spinsante, S., Zdravevski, E., Lameski, P. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 401. Springer, Cham. https://doi.org/10.1007/978-3-030-91421-9_3

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  • DOI: https://doi.org/10.1007/978-3-030-91421-9_3

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