Skip to main content
SpringerLink
Log in

A Survey of Process Mining Competitions: The BPI Challenges 2011–2018

  • Conference paper
  • First Online:
Business Process Management Workshops (BPM 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 362))

Included in the following conference series:

Abstract

In recent years, several advances in the field of process mining, and even data science in general, have come from competitions where participants are asked to analyze a given dataset or event log. Besides providing significant insights about a specific business process, these competitions have also served as a valuable opportunity to test a wide range of process mining techniques in a setting that is open to all participants, from academia to industry. In this work, we conduct a survey of process mining competitions, namely the Business Process Intelligence Challenge, from 2011 to 2018. We focus on the methods, tools and techniques that were used by all participants in order to analyze the published event logs. From this survey, we develop a comparative analysis that allows us to identify the most popular tools and techniques, and to realize that data mining and machine learning are playing an increasingly important role in process mining competitions.

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

References

  1. Bose, R.P.J.C., van der Aalst, W.M.P.: Analysis of patient treatment procedures. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 165–166. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_17

    Chapter  Google Scholar 

  2. Li, J., Bose, R.P.J.C., van der Aalst, W.M.P.: Mining context-dependent and interactive business process maps using execution patterns. In: zur Muehlen, M., Su, J. (eds.) BPM 2010. LNBIP, vol. 66, pp. 109–121. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20511-8_10

    Chapter  Google Scholar 

  3. Bose, R.P.J.C., van der Aalst, W.M.P.: Trace alignment in process mining: opportunities for process diagnostics. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 227–242. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15618-2_17

    Chapter  Google Scholar 

  4. Bautista, A.D., Wangikar, L., Akbar, S.M.K.: Process mining-driven optimization of a consumer loan approvals process. In: La Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 219–220. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36285-9_24

    Chapter  Google Scholar 

  5. Kang, C.J., et al.: Process mining-based understanding and analysis of Volvo IT’s incident and problem management processes. In: CEUR Workshop Proceedings, vol. 1052 (2013)

    Google Scholar 

  6. Buhler, P., et al.: Service desk and incident impact patterns following ITIL change implementation. In: BPI Challenge 2014 (2014)

    Google Scholar 

  7. Cacciola, G., Conforti, R., Nguyen, H.: Rabobank: a process mining case study BPI challenge 2014 report. In: BPI Challenge 2014 (2014)

    Google Scholar 

  8. van der Ham, U.: Benchmarking of five dutch municipalities with process mining techniques reveals opportunities for improvement. In: BPI Challenge 2015 (2015)

    Google Scholar 

  9. Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M.: Handling concept drift in process mining. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 391–405. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21640-4_30

    Chapter  Google Scholar 

  10. Teinemaa, I., Leontjeva, A., Masing, K.-O.: BPIC 2015: diagnostics of building permit application process in dutch municipalities. In: BPI Challenge 2015 (2015)

    Google Scholar 

  11. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. In: Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms (1998)

    Google Scholar 

  12. Weijters, A.J.M.M., van der Aalst, W.M.P., Alves de Medeiros, A.K.: Process Mining with the HeuristicsMiner Algorithm (2006)

    Google Scholar 

  13. van der Ham, U.: Marking up the right tree: understanding the customer process at UWV. In: BPI Challenge 2016 (2016)

    Google Scholar 

  14. Chen, Y., Argentinis, E., Weber, G.: IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin. Ther. 38 (2016). https://doi.org/10.1016/j.clinthera.2015.12.001

  15. Dadashnia, S., Niesen, T., Hake, P., Fettke, P., Mehdiyev, N., Evermann, J.: Identification of distinct usage patterns and prediction of customer behavior. In: BPI Challenge 2016 (2016)

    Google Scholar 

  16. Evermann, J., Rehse, J.-R., Fettke, P.: A deep learning approach for predicting process behaviour at runtime. In: Dumas, M., Fantinato, M. (eds.) BPM 2016. LNBIP, vol. 281, pp. 327–338. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58457-7_24

    Chapter  Google Scholar 

  17. Veiga, G.M., Ferreira, D.R.: Understanding spaghetti models with sequence clustering for ProM. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 92–103. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12186-9_10

    Chapter  Google Scholar 

  18. Povalyaeva, E., Khamitov, I., Fomenko, A.: BPIC 2017: density analysis of the interaction with clients. In: BPI Challenge 2017 (2017)

    Google Scholar 

  19. Rodrigues, A.M.B., et al.: Stairway to value: mining a loan application process. In: BPI Challenge 2017 (2017)

    Google Scholar 

  20. Blevi, L., Robbrecht, J., Delporte, L.: Process mining on the loan application process of a Dutch Financial Institute. In: BPI Challenge 2017 (2017)

    Google Scholar 

  21. Brils, J.H.H., van den Elsen, N.A.F., de Priester, J., Slooff, T.A.: Business process intelligence challenge 2018: analysis and prediction of undesired outcomes. In: BPI Challenge 2018 (2018)

    Google Scholar 

  22. Pauwels, S., Calders, T.: Detecting and explaining drifts in yearly grant applications. In: BPI Challenge 2018 (2018)

    Google Scholar 

  23. Wangikar, L., Dhuwalia, S., Yadav, A., Dikshit, B., Yadav, D.: Faster payments to farmers: analysis of the direct payments process of EU’s agricultural guarantee fund. In: BPI Challenge 2018 (2018)

    Google Scholar 

  24. Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75183-0_24

    Chapter  Google Scholar 

  25. Song, M., Günther, Christian W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00328-8_11

    Chapter  Google Scholar 

  26. Song, M., van der Aalst, W.M.P.: Towards comprehensive support for organizational mining. Decis. Support Syst. 46, 300–317 (2008)

    Article  Google Scholar 

  27. Mans, R.S., Schonenberg, M.H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Application of process mining in healthcare – a case study in a dutch hospital. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2008. CCIS, vol. 25, pp. 425–438. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-92219-3_32

    Chapter  Google Scholar 

  28. 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 

Download references

Author information

Authors and Affiliations

  1. Instituto Superior Técnico (IST), University of Lisbon, Lisbon, Portugal

    Iezalde F. Lopes & Diogo R. Ferreira

Authors
  1. Iezalde F. Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diogo R. Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Diogo R. Ferreira .

Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler, Trento, Italy

    Chiara Di Francescomarino

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Remco Dijkman

  3. University of Vienna, Vienna, Austria

    Uwe Zdun

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

Lopes, I.F., Ferreira, D.R. (2019). A Survey of Process Mining Competitions: The BPI Challenges 2011–2018. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds) Business Process Management Workshops. BPM 2019. Lecture Notes in Business Information Processing, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-37453-2_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37453-2_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37452-5

  • Online ISBN: 978-3-030-37453-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation