Skip to main content
SpringerLink
Log in

Simplification of Complex Process Models by Abstracting Infrequent Behaviour

  • Conference paper
  • First Online:
Book cover Service-Oriented Computing (ICSOC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11895))

Included in the following conference series:

Abstract

Several simplification techniques have been proposed in process mining to improve the interpretability of complex processes, such as the structural simplification of the model or the simplification of the log. However, obtaining a comprehensible model explaining the behaviour of unstructured large processes is still an open challenge. In this paper, we present WoSimp, a novel algorithm to simplify processes by abstracting the infrequent behaviour from the logs, allowing to discover a simpler process model. This algorithm has been validated with more than 10 complex real processes, most of them from Business Process Management Challenges. Experiments show that WoSimp simplifies the process log and allows to discover a better process model than the state of the art techniques.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Notes

  1. 1.

    The algorithm, datasets and results can be downloaded from http://tec.citius.usc.es/processmining/WoSimp/.

  2. 2.

    Using plugin Matrix Filter in ProM with Mean as the Threshold adjusting Method.

  3. 3.

    Using the plugin Activity Filter: Indirect Entropy optimized with Greedy Search in ProM [17].

  4. 4.

    Activity Filter takes more than 24 h to converge in datasets with more than 300 activities, thus, no results of this technique are shown in those datasets.

References

  1. van der Aalst, W.M.P.: Process Mining - Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4

    Book  Google Scholar 

  2. Polyvyanyy, A., García-Bañuelos, L., Dumas, M.: Structuring acyclic process models. Inf. Syst. 37(6), 518–538 (2012)

    Article  Google Scholar 

  3. Fahland, D., van der Aalst, W.M.P.: Simplifying discovered process models in a controlled manner. Inf. Syst. 38(4), 585–605 (2013)

    Article  Google Scholar 

  4. de San Pedro, J., Carmona, J., Cortadella, J.: Log-based simplification of process models. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 457–474. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23063-4_30

    Chapter  Google Scholar 

  5. Sani, M.F., van Zelst, S.J., van der Aalst, W.M.P.: Improving process discovery results by filtering outliers using conditional behavioural probabilities. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 216–229. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_16

    Chapter  Google Scholar 

  6. Tax, N., Sidorova, N., van der Aalst, W.M.P.: Discovering more precise process models from event logs by filtering out chaotic activities. J. Intell. Inf. Syst. 52(1), 107–139 (2019)

    Article  Google Scholar 

  7. Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J.: From low-level events to activities - a pattern-based approach. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 125–141. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_8

    Chapter  Google Scholar 

  8. Mannhardt, F., Tax, N.: Unsupervised event abstraction using pattern abstraction and local process models. In: Gulden, J., Nurcan, S., et al. (eds.) BPMDS 2017. CEUR Workshop Proceedings, vol. 1859, pp. 55–63. CEUR-WS.org (2017)

    Google Scholar 

  9. Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P.: Mining local process models. CoRR abs/1606.06066 (2016)

    Google Scholar 

  10. Chapela-Campa, D., Mucientes, M., Lama, M.: Mining frequent patterns in process models. Inf. Sci. 472, 235–257 (2019)

    Article  Google Scholar 

  11. Desel, J., Reisig, W.: Place/transition Petri Nets. In: Reisig, W., Rozenberg, G. (eds.) ACPN 1996. LNCS, vol. 1491, pp. 122–173. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-65306-6_15

    Chapter  MATH  Google Scholar 

  12. Mannhardt, F. (Felix): Sepsis cases - event log (2016)

    Google Scholar 

  13. Van Dongen, B.: Real-life event logs - hospital log (2011)

    Google Scholar 

  14. Van Dongen, B.: BPI Challenge 2012 (2012)

    Google Scholar 

  15. Steeman, W.: BPI Challenge 2013 (2013)

    Google Scholar 

  16. Van Dongen, B.: BPI Challenge 2015 (2015)

    Google Scholar 

  17. van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005). https://doi.org/10.1007/11494744_25

    Chapter  Google Scholar 

  18. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38697-8_17

    Chapter  Google Scholar 

  19. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 66–78. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06257-0_6

    Chapter  Google Scholar 

  20. Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Conformance checking using cost-based fitness analysis. In: EDOC 2011, pp. 55–64. IEEE Computer Society (2011)

    Google Scholar 

  21. vanden Broucke, S.K.L.M., Weerdt, J.D., Vanthienen, J., Baesens, B.: Determining process model precision and generalization with weighted artificial negative events. IEEE Trans. Knowl. Data Eng. 26(8), 1877–1889 (2014)

    Article  Google Scholar 

  22. vanden Broucke, S.K.L.M., Weerdt, J.D., Vanthienen, J., Baesens, B.: A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM. In: IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013, pp. 254–261. IEEE (2013)

    Google Scholar 

Download references

Acknowledgments

This research was funded by the Spanish Ministry of Economy and Competitiveness under grant TIN2017-84796-C2-1-R, and the Galician Ministry of Education, Culture and Universities under grant ED431G/08. These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program). D. Chapela-Campa is supported by the Spanish Ministry of Education, under the FPU national plan (FPU16/04428).

Author information

Authors and Affiliations

  1. Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    David Chapela-Campa, Manuel Mucientes & Manuel Lama

Authors
  1. David Chapela-Campa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Mucientes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manuel Lama
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Chapela-Campa .

Editor information

Editors and Affiliations

  1. Laboratory for Analysis and Architecture, Toulouse, France

    Sami Yangui

  2. National Engineering School of Sfax, Sfax, Tunisia

    Ismael Bouassida Rodriguez

  3. Laboratory for Analysis and Architecture, Toulouse, France

    Khalil Drira

  4. RMIT University, Melbourne, VIC, Australia

    Zahir Tari

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chapela-Campa, D., Mucientes, M., Lama, M. (2019). Simplification of Complex Process Models by Abstracting Infrequent Behaviour. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33702-5_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33701-8

  • Online ISBN: 978-3-030-33702-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation