Skip to main content
SpringerLink
Log in

Freezing Sub-models During Incremental Process Discovery

  • Conference paper
  • First Online:
Conceptual Modeling (ER 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13011))

Included in the following conference series:

Abstract

Process discovery aims to learn a process model from observed process behavior. From a user’s perspective, most discovery algorithms work like a black box. Besides parameter tuning, there is no interaction between the user and the algorithm. Interactive process discovery allows the user to exploit domain knowledge and to guide the discovery process. Previously, an incremental discovery approach has been introduced where a model, considered to be under “construction”, gets incrementally extended by user-selected process behavior. This paper introduces a novel approach that additionally allows the user to freeze model parts within the model under construction. Frozen sub-models are not altered by the incremental approach when new behavior is added to the model. The user can thus steer the discovery algorithm. Our experiments show that freezing sub-models can lead to higher quality models.

An extended version is available online: https://arxiv.org/abs/2108.00215.

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 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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

Notes

  1. 1.

    https://github.com/fit-daniel-schuster/Freezing-Sub-Models-During-Incr-PD.

References

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

    Book  Google Scholar 

  2. Armas Cervantes, A., van Beest, N.R.T.P., La Rosa, M., Dumas, M., García-Bañuelos, L.: Interactive and incremental business process model repair. In: Panetto, H., et al. (eds.) OTM 2017. LNCS, vol. 10573, pp. 53–74. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69462-7_5

    Chapter  Google Scholar 

  3. Carmona, J., van Dongen, B.F., Solti, A., Weidlich, M.: Conformance Checking - Relating Processes and Models. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-99414-7

    Book  Google Scholar 

  4. Dixit, P.M., Buijs, J.C.A.M., van der Aalst, W.M.P., Hompes, B.F.A., Buurman, J.: Using domain knowledge to enhance process mining results. In: Ceravolo, P., Rinderle-Ma, S. (eds.) SIMPDA 2015. LNBIP, vol. 244, pp. 76–104. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53435-0_4

    Chapter  Google Scholar 

  5. Dixit, P.M., Verbeek, H.M.W., Buijs, J.C.A.M., van der Aalst, W.M.P.: Interactive data-driven process model construction. In: Trujillo, J.C., et al. (eds.) ER 2018. LNCS, vol. 11157, pp. 251–265. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00847-5_19

    Chapter  Google Scholar 

  6. Fahland, D., van der Aalst, W.M.P.: Repairing process models to reflect reality. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 229–245. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32885-5_19

    Chapter  Google Scholar 

  7. Greco, G., Guzzo, A., Lupia, F., Pontieri, L.: Process discovery under precedence constraints. ACM Trans. Knowl. Discov. Data 9(4), 1–39 (2015). https://doi.org/10.1145/2710020

    Article  Google Scholar 

  8. 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 

  9. de Leoni, M., Mannhardt, F.: Road traffic fine management process (2015). https://doi.org/10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5

  10. Rembert, A.J., Omokpo, A., Mazzoleni, P., Goodwin, R.T.: Process discovery using prior knowledge. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 328–342. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45005-1_23

    Chapter  Google Scholar 

  11. Schuster, D., van Zelst, S.J., van der Aalst, W.M.P.: Incremental discovery of hierarchical process models. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds.) RCIS 2020. LNBIP, vol. 385, pp. 417–433. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50316-1_25

    Chapter  Google Scholar 

  12. Schuster, D., van Zelst, S.J., van der Aalst, W.M.P.: Cortado—an interactive tool for data-driven process discovery and modeling. In: Buchs, D., Carmona, J. (eds.) PETRI NETS 2021. LNCS, vol. 12734, pp. 465–475. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-76983-3_23

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany

    Daniel Schuster, Sebastiaan J. van Zelst & Wil M. P. van der Aalst

  2. RWTH Aachen University, Aachen, Germany

    Daniel Schuster, Sebastiaan J. van Zelst & Wil M. P. van der Aalst

Authors
  1. Daniel Schuster
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sebastiaan J. van Zelst
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wil M. P. van der Aalst
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Schuster .

Editor information

Editors and Affiliations

  1. School of Computing and IT, University of Wollongong, Wollongong, NSW, Australia

    Aditya Ghose

  2. Department of Computer Science and Engineering, Chalmers | University of Gothenburg, Gothenburg, Sweden

    Jennifer Horkoff

  3. Universidade Federal do Espírito Santo, Vitória, Brazil

    Vítor E. Silva Souza

  4. Faculty of Business Administration, Memorial University of Newfoundland, St. John's, NL, Canada

    Jeffrey Parsons

  5. Faculty of Business Administration, Memorial University of Newfoundland, St. John's, NL, Canada

    Joerg Evermann

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Schuster, D., van Zelst, S.J., van der Aalst, W.M.P. (2021). Freezing Sub-models During Incremental Process Discovery. In: Ghose, A., Horkoff, J., Silva Souza, V.E., Parsons, J., Evermann, J. (eds) Conceptual Modeling. ER 2021. Lecture Notes in Computer Science(), vol 13011. Springer, Cham. https://doi.org/10.1007/978-3-030-89022-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-89022-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89021-6

  • Online ISBN: 978-3-030-89022-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation