Skip to main content

The Interplay Between High-Level Problems and the Process Instances that Give Rise to Them

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

Abstract

Business processes may face a variety of problems due to the number of tasks that need to be handled within short time periods, resources’ workload and working patterns, as well as bottlenecks. These problems may arise locally and be short-lived, but as the process is forced to operate outside its standard capacity, the effect on the underlying process instances can be costly. We use the term high-level behavior to cover all process behavior which can not be captured in terms of the individual process instances. The natural question arises as to how the characteristics of cases relate to the high-level behavior they give rise to. In this work, we first show how to detect and correlate observations of high-level problems, as well as determine the corresponding (non-)participating cases. Then we show how to assess the connection between any case-level characteristic and any given detected sequence of high-level problems. Applying our method on the event data of a real loan application process revealed which specific combinations of delays, batching and busy resources at which particular parts of the process correlate with an application’s duration and chance of a positive outcome.

We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.

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.

    https://data.4tu.nl/articles/dataset/BPI_Challenge_2017/12696884.

  2. 2.

    https://github.com/biankabakullari/hlem-framework.

References

  1. van der Aalst, W.M.P.: Process Mining: Data science in Action. Tech. rep. (2014)

    Google Scholar 

  2. Bakullari, B., van der Aalst, W.M.P.: High-level event mining: A framework. In: 2022 4th International Conference on Process Mining (ICPM) (2022)

    Google Scholar 

  3. Bozorgi, Z.D., Teinemaa, I., Dumas, M., La Rosa, M., Polyvyanyy, A.: Process mining meets causal machine learning: discovering causal rules from event logs. In: 2020 2nd International Conference on Process Mining (ICPM) (2020)

    Google Scholar 

  4. Bozorgi, Z.D., Teinemaa, I., Dumas, M., Rosa, M.L., Polyvyanyy, A.: Prescriptive process monitoring for cost-aware cycle time reduction. In: 2021 3rd International Conference on Process Mining (ICPM) (2021)

    Google Scholar 

  5. Denisov, V., Belkina, E., Fahland, D., van der Aalst, W.M.P.: The performance spectrum miner: visual analytics for fine-grained performance analysis of processes. In: International Conference on Business Process Management (BPM) (2018)

    Google Scholar 

  6. Denisov, V., Fahland, D., van der Aalst, W.M.P.: Unbiased, fine-grained description of processes performance from event data. In: International Conference on Business Process Management (BPM) (2018)

    Google Scholar 

  7. Dubinsky, Y., Soffer, P., Hadar, I.: Detecting cross-case associations in an event log: toward a pattern-based detection. Softw. Syst. Model (2023). https://doi.org/10.1007/s10270-023-01100-w

  8. Klijn, E.L., Fahland, D.: Performance mining for batch processing using the performance spectrum. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds.) BPM 2019. LNBIP, vol. 362, pp. 172–185. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37453-2_15

    Chapter  Google Scholar 

  9. Klijn, E.L., Fahland, D.: Identifying and reducing errors in remaining time prediction due to inter-case dynamics. In: 2020 2nd International Conference on Process Mining (ICPM) (2020)

    Google Scholar 

  10. Martin, N., Pufahl, L., Mannhardt, F.: Detection of batch activities from event logs. Inf. Syst. 95, 77–92 (2021)

    Article  Google Scholar 

  11. Pika, A., Ouyang, C., ter Hofstede, A.: Configurable batch-processing discovery from event logs. ACM Trans. Manag. Inf. Syst. 13, 28 (2022)

    Google Scholar 

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

    Google Scholar 

  13. Senderovich, A., Beck, J., Gal, A., Weidlich, M.: Congestion graphs for automated time predictions. Proc. AAAI Conf. Artif. Intell. 33, 4854–4861 (2019)

    Google Scholar 

  14. Senderovich, A., Francescomarino, C.D., Maggi, F.M.: From knowledge-driven to data-driven inter-case feature encoding in predictive process monitoring. Inf. Syst. 84, 255–264 (2019)

    Article  Google Scholar 

  15. Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining for delay prediction in multi-class service processes. Inf. Syst. 53, 278–295 (2015)

    Article  Google Scholar 

  16. Suriadi, S., Wynn, M., Xu, J., van der Aalst, W., ter Hofstede, A.: Discovering work Prioritisation patterns from event logs. Decis. Support Syst. 100, 77–92 (2017)

    Article  Google Scholar 

  17. Toosinezhad, Z., Fahland, D., Köroglu, Ö., van der Aalst, W.M.P.: Detecting system-level behavior leading to dynamic bottlenecks. In: 2020 2nd International Conference on Process Mining (ICPM) (2020)

    Google Scholar 

  18. van Hulzen, G.A., Li, C.Y., Martin, N., van Zelst, S.J., Depaire, B.: Mining context-aware resource profiles in the presence of multitasking. Artif. Intell. Med. 134, 102434 (2022)

    Article  Google Scholar 

  19. Wimbauer, A., Richter, F., Seidl, T.: PErrCas: process error cascade mining in trace streams. In: Munoz-Gama, J., Lu, X. (eds.) ICPM 2021. LNBIP, vol. 433, pp. 224–236. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98581-3_17

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bianka Bakullari .

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

Bakullari, B., Thoor, J.v., Fahland, D., van der Aalst, W.M.P. (2023). The Interplay Between High-Level Problems and the Process Instances that Give Rise to Them. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds) Business Process Management Forum. BPM 2023. Lecture Notes in Business Information Processing, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-41623-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-41623-1_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41622-4

  • Online ISBN: 978-3-031-41623-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics