SpringerLink
Log in

Navigating Event Abstraction in Process Mining: A Comprehensive Analysis of Sub-problems, Data, and Process Characteristic Considerations

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

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 492))

Included in the following conference series:

  • 513 Accesses

Abstract

Process mining technologies assume event logs with an appropriate level of granularity, but many systems generate low-level event logs, resulting in complex process models. Event abstraction addresses this issue by transforming low-level event logs into abstracted event logs, enabling the derivation of business-level process models. However, practitioners often struggle to choose suitable event abstraction methods. This is primarily due to the lack of comparative studies that analyze the differences between methods and the insufficient information regarding the data and relevant process characteristics to be considered. This study conducts a comprehensive literature review on event abstraction to overcome these challenges. The review focuses on summarizing specific sub-problems in event abstraction, identifying types of data that can be utilized, and highlighting important process characteristics that should be considered. The insights and guidance provided by this review will be valuable to practitioners seeking to select and implement effective event abstraction techniques.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /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 (Brazil)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (Brazil)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (Brazil)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** 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. Van der Aalst, W.: Process mining: data science in action (2016)

    Google Scholar 

  2. Li, C.Y., Van Zelst, S.J., Van Der Aalst, W.M.P.: An activity instance based hierarchical framework for event abstraction. In: La Rosa, M., Loos, P., Pastor, O. (eds.) Business Process Management. BPM 2016. LNCS, vol. 9850, pp. 160–167. Springer, Cham (2021). https://doi.org/10.1007/978-3-319-45348-4_8

  3. 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.) Business Process Management. BPM 2016. LNCS, vol. 9850, pp. 125–141. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_8

  4. Diba, K., Batoulis, K., Weidlich, M., Weske, M.: Extraction, correlation, and abstraction of event data for process mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 10, 1–24 (2020)

    Article  Google Scholar 

  5. Marin-Castro, H.M., Tello-Leal, E.: Event log preprocessing for process mining: a review. Appl. Sci. (Switzerland). 11, 1–29 (2021)

    Google Scholar 

  6. van Zelst, S.J., Mannhardt, F., de Leoni, M., Koschmider, A.: Event abstraction in process mining: literature review and taxonomy. Granul. Comput. 6, 719–736 (2021)

    Article  Google Scholar 

  7. Mannhardt, F., Tax, N.: Unsupervised event abstraction using pattern abstraction and local process models. ar**v preprint ar**v:1704.03520 (2017)

  8. Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J.: Guided process discovery – a pattern-based approach. Inf. Syst. 76, 1–18 (2018)

    Article  Google Scholar 

  9. Ferreira, D.R., Szimanski, F., Ralha, C.G.: Improving process models by mining map**s of low-level events to high-level activities. J. Intell. Inf. Syst. 43, 379–407 (2014)

    Article  Google Scholar 

  10. De Leoni, M., Dündar, S.: Event-log abstraction using batch session identification and clustering. In: Proceedings of the ACM Symposium on Applied Computing, pp. 36–44 (2020)

    Google Scholar 

  11. Van Eck, M.L., Sidorova, N., Van Der Aalst, W.M.P.: Enabling process mining on sensor data from smart products. In: Proceedings - International Conference on Research Challenges in Information Science. 2016-Augus, (2016)

    Google Scholar 

  12. Günther, C.W., Rozinat, A., Van Der Aalst, W.M.P.: Activity mining by global trace segmentation. In: Daniel, F., Sheng, Q., Motahari, H. (eds.) Business Process Management Workshops. BPM 2018. LNBIP, vol. 342, pp. 128–139. Springer, Cham (2010). https://doi.org/10.1007/978-3-030-11641-5_1

  13. Rehse, J.-R., Fettke, P.: Clustering business process activities for identifying reference model components. In: Daniel, F., Sheng, Q., Motahari, H. (eds.) Business Process Management Workshops. BPM 2018. LNBIP, vol. 342, pp. 5–17. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11641-5_1

  14. Baier, T., Mendling, J., Weske, M.: Bridging abstraction layers in process mining. Inf. Syst. 46, 123–139 (2014)

    Article  Google Scholar 

  15. Baier, T., Di Ciccio, C., Mendling, J., Weske, M.: Matching events and activities by integrating behavioral aspects and label analysis. Softw. Syst. Model. 17, 573–598 (2018)

    Article  Google Scholar 

  16. Sánchez-Charles, D., Carmona, J., Muntés-Mulero, V., Solé, M.: Reducing event variability in logs by clustering of word embeddings. In: Teniente, E., Weidlich, M. (eds.) Business Process Management Workshops. BPM 2017. LNBIP, vol. 308. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_14

  17. Andritsos, P.: CJM-ab : Abstracting Customer Journey, vol. 1, pp. 49–56 (2018)

    Google Scholar 

  18. Richetti, P.H.P., Baião, F.A., Santoro, F.M.: Declarative process mining: reducing discovered models complexity by pre-processing event logs. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) Business Process Management. BPM 2014. LNCS, vol. 8659, pp. 400–407. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10172-9_28

  19. Brzychczy, E., Trzcionkowska, A.: Process-oriented approach for analysis of sensor data from longwall monitoring system. Adv. Intell. Syst. Comput. 835, 611–621 (2019)

    Article  Google Scholar 

  20. Van Dongen, B.F., Adriansyah, A.: Process mining: fuzzy clustering and performance visualization. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) Business Process Management Workshops. BPM 2009. LNBIP, vol. 43, PP. 158–169. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12186-9_15

  21. Begicheva, A.A., Lomazov, I.A.: Discovering high-level process models from event logs. Model. Anal. Inf. Syst. 24, 125–140 (2017)

    Article  Google Scholar 

  22. Leonardi, G., Striani, M., Quaglini, S., Cavallini, A., Montani, S.B.: Towards semantic process mining through knowledge-based trace abstraction. In: Ceravolo, P., van Keulen, M., Stoffel, K. (eds.) Data-Driven Process Discovery and Analysis. SIMPDA 2017. LNBIP, vol. 340, pp. 45–64. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11638-5_3

  23. Folino, F., Guarascio, M., Pontieri, L.: Mining multi-variant process models from low-level logs. In: Abramowicz, W. (eds.) Business Information Systems. BIS 2015. LNBIP, vol. 208, pp. 165–177. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19027-3_14

  24. Alharbi, A., Bulpitt, A., Johnson, O.: Towards unsupervised detection of process models in healthcare. In: Ugon, A., Karlsson, D., Dlein, gunnar O., and Moen, A. (eds.) Building Continents of Knowledge in Oceans of Data: The Future of Co-Create eHealth, pp. 381–385. IOS Press (2018)

    Google Scholar 

  25. Fazzinga, B., Flesca, S., Furfaro, F., Pontieri, L.: Process discovery from low-level event logs. In: Krogstie, J., Reijers, H. (eds.) Advanced Information Systems Engineering. CAiSE 2018. LNCS, vol. 10816, PP. 257–273. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91563-0_16

  26. Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.: Mining process model descriptions of daily life through event abstraction. Stud. Comput. Intell. 751, 83–104 (2018)

    Article  Google Scholar 

  27. Senderovich, A., Rogge-Solti, A., Gal, A., Mendling, J., Mandelbaum, A.: The ROAD from sensor data to process instances via interaction mining. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) Advanced Information Systems Engineering. CAiSE 2016. LNCS, vol. 9694, pp. 257–273. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39696-5_16

  28. Fazzinga, B., Flesca, S., Furfaro, F., Masciari, E., Pontieri, L.: Efficiently interpreting traces of low level events in business process logs. Inf. Syst. 73, 1–24 (2018)

    Article  Google Scholar 

  29. Bose, R.P.J.C., Maggi, F.M., Van Der Aalst, W.M.P.: Enhancing declare maps based on event correlations. In: Daniel, F., Wang, J., Weber, B. (eds.) Business Process Management. LNCS, vol. 8094, pp. 97–112. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40176-3_9

  30. 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.) Business Process Management Workshops. BPM 2010. LNBIP, vol. 66, pp. 109–121. Springer, Berlin, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20511-8_10

  31. Folino, F., Guarascio, M., Pontieri, L.: Mining predictive process models out of low-level multidimensional logs. In: Jarke, M., et al. (eds.) Advanced Information Systems Engineering. CAiSE 2014. LNCS, vol. 8484, pp. 533–547. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_36

  32. Tello, G., Gianini, G., Mizouni, R., Damiani, E.: Machine learning-based framework for log-lifting in business process. In: Hildebrandt, T., van Dongen, B., Röglinger, M., Mendling, J. (eds.) Business Process Management. BPM 2019. LNCS, vol. 11675. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26619-6_16

  33. Sun, Y., Bauer, B.: A graph and trace clustering-based approach for abstracting mined business process models. In: ICEIS 2016 - Proceedings of the 18th International Conference on Enterprise Information Systems, vol. 1, pp. 63–74 (2016)

    Google Scholar 

  34. Ferreira, D.R., Szimanski, F., Ralha, C.G.: Mining the low-level behaviour of agents in high-level business processes. Int. J. Bus. Process. Integr. Manag. 6, 146–166 (2013)

    Article  Google Scholar 

Download references

Acknowledgment

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-2018-0-01441) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) and by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI23C0061).

Author information

Authors and Affiliations

  1. Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South Korea

    Jungeun Lim & Minseok Song

Authors
  1. Jungeun Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minseok Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Minseok Song .

Editor information

Editors and Affiliations

  1. KU Leuven, Leuven, Belgium

    Jochen De Weerdt

  2. TUM School of Computation, Information and Technology, Heilbronn, Germany

    Luise Pufahl

Rights and permissions

Reprints and permissions

Copyright information

© 2024 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

Lim, J., Song, M. (2024). Navigating Event Abstraction in Process Mining: A Comprehensive Analysis of Sub-problems, Data, and Process Characteristic Considerations. In: De Weerdt, J., Pufahl, L. (eds) Business Process Management Workshops. BPM 2023. Lecture Notes in Business Information Processing, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-50974-2_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50974-2_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50973-5

  • Online ISBN: 978-3-031-50974-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation