Skip to main content

Interactive Multi-interest Process Pattern Discovery

  • Conference paper
  • First Online:
Business Process Management (BPM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14159))

Included in the following conference series:

Abstract

Process pattern discovery methods (PPDMs) aim at identifying patterns of interest to users. Existing PPDMs typically are unsupervised and focus on a single dimension of interest, such as discovering frequent patterns. We present an interactive multi-interest-driven framework for process pattern discovery aimed at identifying patterns that are optimal according to a multi-dimensional analysis goal. The proposed approach is iterative and interactive, thus taking experts’ knowledge into account during the discovery process. The paper focuses on a concrete analysis goal, i.e., deriving process patterns that affect the process outcome. We evaluate the approach on real-world event logs in both interactive and fully automated settings. The approach extracted meaningful patterns validated by expert knowledge in the interactive setting. Patterns extracted in the automated settings consistently led to prediction performance comparable to or better than patterns derived considering single-interest dimensions without requiring user-defined thresholds.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Please note that we added the black dots only for the sake of clarity in the visualization of a concurrent pattern and they do not belong to the extended pattern.

  2. 2.

    https://github.com/MozhganVD/InteractivePatternDetection.

References

  1. Acheli, M., Grigori, D., Weidlich, M.: Discovering and analyzing contextual behavioral patterns from event logs. IEEE Transactions on Knowledge and Data Engineering 34(12), 5708–5721 (2021)

    Article  Google Scholar 

  2. Martin Atzmueller, Stefan Bloemheuvel, and Benjamin Kloepper. A framework for human-centered exploration of complex event log graphs. In International Conference on Discovery Science, pages 335–350, 2019

    Google Scholar 

  3. Elisabetta Benevento, Davide Aloini, and Wil MP van der Aalst. How can interactive process discovery address data quality issues in real business settings? evidence from a case study in healthcare. Journal of Biomedical Informatics, 2022

    Google Scholar 

  4. Benevento, E., Dixit, P.M., Sani, M.F., Aloini, D., van der Aalst, W.M.P.: Evaluating the Effectiveness of Interactive Process Discovery in Healthcare: A Case Study. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds.) BPM 2019. LNBIP, vol. 362, pp. 508–519. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37453-2_41

    Chapter  Google Scholar 

  5. S. Borzsony, D. Kossmann, and K. Stocker. The skyline operator. In Proceedings 17th International Conference on Data Engineering, pages 421–430, 2001

    Google Scholar 

  6. RP Jagadeesh Chandra Bose and Wil MP Van der Aalst. Abstractions in process mining: A taxonomy of patterns. In International Conference on Business Process Management, pages 159–175, 2009

    Google Scholar 

  7. RP Jagadeesh Chandra Bose and Wil MP van der Aalst. Trace clustering based on conserved patterns: Towards achieving better process models. In International Conference on Business Process Management, pages 170–181, 2009

    Google Scholar 

  8. Cheung, Y., Jia, H.: Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number. Pattern Recognition 46(8), 2228–2238 (2013)

    Article  MATH  Google Scholar 

  9. Diamantini, C., Genga, L., Potena, D.: Behavioral process mining for unstructured processes. Journal of Intelligent Information Systems 47(1), 5–32 (2016). https://doi.org/10.1007/s10844-016-0394-7

    Article  Google Scholar 

  10. Diamantini, C., Genga, L., Potena, D., van der Aalst, W.: Building instance graphs for highly variable processes. Expert Systems with Applications 59, 101–118 (2016)

    Article  Google Scholar 

  11. Dirk Fahland. Multi-dimensional process analysis. In Business Process Management: 20th International Conference, BPM 2022, pages 27–33. Springer, 2022

    Google Scholar 

  12. Fang, W., Zhang, Q., Sun, J., Xiaojun, W.: Mining high quality patterns using multi-objective evolutionary algorithm. IEEE Transactions on Knowledge and Data Engineering 34(8), 3883–3898 (2020)

    Article  Google Scholar 

  13. Christian W Günther and Wil MP Van Der Aalst. Fuzzy mining-adaptive process simplification based on multi-perspective metrics. In International conference on business process management, pages 328–343, 2007

    Google Scholar 

  14. Huang, Z., Xudong, L., Duan, H.: On mining clinical pathway patterns from medical behaviors. Artificial intelligence in medicine 56(1), 35–50 (2012)

    Article  Google Scholar 

  15. Hwang, S.-Y., Wei, C.-P., Yang, W.-S.: Discovery of temporal patterns from process instances. Computers in industry 53(3), 345–364 (2004)

    Article  Google Scholar 

  16. Maikel Leemans and Wil MP van der Aalst. Discovery of frequent episodes in event logs. In International symposium on data-driven process discovery and analysis, pages 1–31, 2014

    Google Scholar 

  17. Sander JJ Leemans, Sebastiaan J van Zelst, and Xixi Lu. Partial-order-based process mining: a survey and outlook. Knowledge and Information Systems, 65(1), 1–29, 2023

    Google Scholar 

  18. Xixi Lu, Dirk Fahland, Robert Andrews, Suriadi Suriadi, Moe T Wynn, Arthur HM ter Hofstede, and Wil MP van der Aalst. Semi-supervised log pattern detection and exploration using event concurrence and contextual information. In OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”, pages 154–174, 2017

    Google Scholar 

  19. Xixi Lu, Dirk Fahland, and Wil MP van der Aalst. Conformance checking based on partially ordered event data. In Business Process Management Workshops: BPM 2014 International Workshops, pages 75–88, 2015

    Google Scholar 

  20. Felix Mannhardt and Niek Tax. Unsupervised event abstraction using pattern abstraction and local process models. arXiv preprint arXiv:1704.03520, 2017

  21. Hoang Nguyen, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, and Suriadi Suriadi. Mining business process deviance: a quest for accuracy. In OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”, pages 436–445, 2014

    Google Scholar 

  22. Niek Tax, Benjamin Dalmas, Natalia Sidorova, Wil MP van der Aalst, and Sylvie Norre. Interest-driven discovery of local process models. Information Systems, 77:105–117, 2018

    Google Scholar 

  23. Niek Tax, Natalia Sidorova, Reinder Haakma, and Wil MP van der Aalst. Mining local process models. Journal of Innovation in Digital Ecosystems, 3(2), 2016

    Google Scholar 

  24. Irene Teinemaa, Marlon Dumas, Marcello La Rosa, and Fabrizio Maria Maggi. Outcome-oriented predictive process monitoring: Review and benchmark. ACM Transactions on Knowledge Discovery from Data, 13(2), 1–57, 2019

    Google Scholar 

  25. Aika Terada, David duVerle, and Koji Tsuda. Significant pattern mining with confounding variables. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 277–289, 2016

    Google Scholar 

  26. Mozhgan Vazifehdoostirani, Laura Genga, and Remco Dijkman. Encoding high-level control-flow construct information for process outcome prediction. In 2022 4th International Conference on Process Mining, pages 48–55, 2022

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mozhgan Vazifehdoostirani .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Vazifehdoostirani, M., Genga, L., Lu, X., Verhoeven, R., van Laarhoven, H., Dijkman, R. (2023). Interactive Multi-interest Process Pattern Discovery. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds) Business Process Management. BPM 2023. Lecture Notes in Computer Science, vol 14159. Springer, Cham. https://doi.org/10.1007/978-3-031-41620-0_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-41620-0_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41619-4

  • Online ISBN: 978-3-031-41620-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics