Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Digitalization of Society, Economics and Management

Part of the book series: Lecture Notes in Information Systems and Organisation ((LNISO,volume 53))

  • 634 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ramirez, R., Melville, N., & Lawler, E. (2010). Information technology infrastructure, organizational process redesign, and business value: An empirical analysis. Decision Support Systems, 49(4), 417–429.

    Article  Google Scholar 

  2. vom Brocke, J., & Mendling, J. (Eds). (2018). Business process management cases. Digital innovation and business transformation in practice (1st ed, p.626). Berlin: Springer.

    Google Scholar 

  3. van der Aalst, W. (2015). Process mining: Data science in action. 2nd edn (p. 477). Springer Science+Business Media, Netherlands.

    Google Scholar 

  4. Jansen-Vullers, M., & Netjes, M. (2006). Business process simulation−a tool survey. In Proceedings of the Workshop and Tutorial on Practical Use of Coloured Petry Nets and the CPN Tools.

    Google Scholar 

  5. van Hee, K. M., Oanea, O., Post, R., Somers, L. J., & van Werf, J. M. E. M. (2006). Yasper: A tool for workflow modeling and analysis. In Proceedings of the 6th International Conference on Application of Concurrency to System Design (pp. 279–282).

    Google Scholar 

  6. Wynn, M., Dumas, M., & Fidge, C. J. (2007). Business process simulation for operational decision support. In Proceedings of the 3rd International Workshop on Business Process Intelligence (BPI 07) in conjunction with Business Process Management Conference.

    Google Scholar 

  7. Verbeek, H. M., Basten, T., & van der Aalst, W. M. P. (2001). Diagnosing workflow processes using woflan. The Computer Journal, 246–279.

    Google Scholar 

  8. Wang, Y., Zacharewicz, G., Traore, M. K., & Chen, D. (2018). An integrative approach to simulation model discovery: Combining system theory, process mining and fuzzy logic. Journal of Intelligent & Fuzzy Systems, 34, 477–490.

    Article  Google Scholar 

  9. Arriagada-Benitez, M., Sepulveda, M., Munoz-Gama, J., & Buijs, J. C. A. M. (2017). Strategies to automatically derive a process model from a configurable process model based on event data. Applied Sciences, 7, 1023–1051.

    Article  Google Scholar 

  10. van der Aalst, W. et al. (2012). Process mining manifesto. In F. Daniel, K. Barkaoui, S. Dustdar (Eds.), Business Process Management Workshops. BPM 2011. Lecture Notes in Business Information Processing (Vol. 99). Berlin, Heidelberg: Springer.

    Google Scholar 

  11. State-of-the-art-process mining in Python Homepage. https://pm4py.fit.fraunhofer.de/. Accessed 11 May 2020.

  12. CatBoost Homepage. https://catboost.ai/. Accessed 11 May 2020.

Download references

Acknowledgements

This research was partially supported by the RFBR (Grant No 20–07–00958) and the Program of Project Group Competition of the HSE University.

Author information

Authors and Affiliations

  1. HSE University, Myasnitskaya 20, Moscow, 101000, Russian Federation

    Valeria Nikitina & Peter Panfilov

Authors
  1. Valeria Nikitina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Panfilov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter Panfilov .

Editor information

Editors and Affiliations

  1. Higher School of Economics, National Research University, Moscow, Russia

    Evgeny Zaramenskikh

  2. HR Management and Psychology, Ural Federal University, Yekaterinburg, Russia

    Alena Fedorova

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nikitina, V., Panfilov, P. (2022). A Practical Study of Process Mining from Event Logs Using Machine Learning and Petry Net Models. In: Zaramenskikh, E., Fedorova, A. (eds) Digitalization of Society, Economics and Management. Lecture Notes in Information Systems and Organisation, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-030-94252-6_13

Download citation

Publish with us

Policies and ethics

Search

Navigation