Nothing Special   »   [go: up one dir, main page]

Skip to main content

Detecting the “Split-Cases” Workaround in Event Logs

  • Conference paper
  • First Online:
Enterprise, Business-Process and Information Systems Modeling (BPMDS 2021, EMMSAD 2021)

Abstract

Workarounds are frequently observed in business processes, where employees intentionally bypass or deviate from procedures and business rules embedded in process models. One of the common workaround behaviors is the “split cases” workaround. This workaround typically takes place in processes where threshold conditions over data items are used by decision rules, determining the process path to be followed. Being familiar with the process rules, employees may decide to split a case whose relevant data value exceeds the threshold into two or more cases whose data value is below the threshold. Doing this they avoid the path which should be followed otherwise – this path may seem undesirable to the employee (e.g., including lengthy approvals), but might be needed for avoiding risks. Detecting such workarounds is hence of importance.

Although the split case workaround is quite common, existing process mining and conformance checking techniques are not geared to detect it in an event log, since each of the cases is conformant by itself, and it is the relation among cases which might indicate this behavior. In this paper we propose an approach for detecting the split cases workaround in event log and report experimentation that has been performed using simulated as well as real life logs.

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
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

Similar content being viewed by others

Notes

  1. 1.

    Code is available at https://github.com/yael935/Split_Cases. The implemented algorithm addresses one uniting attribute, and can be easily generalized to multiple attributes.

References

  1. Alter, S.: Theory of Workarounds. Commun. Assoc. Inf. Syst. 34, 1041–1066 (2014)

    Google Scholar 

  2. Outmazgin, N.: Exploring workaround situations in business processes. In: La. Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 426–437. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36285-9_45

    Chapter  Google Scholar 

  3. Hepzibah, P.: $2.1 billion arms procurement fraud in Nigeria: its impact on national security, peace and sustainable economic development in Jonathan’s administration 2011–2015. Int. J. Arts Sci. 9(2), 225–248 (2016)

    Google Scholar 

  4. Outmazgin, N., Soffer, P.: Business process workarounds: what can and cannot be detected by process mining. In: Nurcan, S., et al. (eds.) BPMDS/EMMSAD -2013. LNBIP, vol. 147, pp. 48–62. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38484-4_5

    Chapter  Google Scholar 

  5. Leitner, M., Mangler, J., Rinderle-Ma, S.: Definition and enactment of instance-spanning process constraints. In: Sean Wang, X., Cruz, I., Delis, A., Huang, G. (eds.) WISE 2012. LNCS, vol. 7651, pp. 652–658. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35063-4_49

    Chapter  Google Scholar 

  6. Winter, K., Stertz, F., Rinderle-Ma, S.: Discovering instance and process spanning constraints from process execution logs. Inf. Syst. 89, 101484 (2020)

    Article  Google Scholar 

  7. Van Der Aalst, W.M.P.: Process mining: overview and opportunities. ACM Trans. Manag. Inf. Syst. (TMIS) 3(2), 1–17 (2012)

    Article  Google Scholar 

  8. De Leoni, M., Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Decomposing conformance checking on Petri nets with data. BPM center report BPM-14-06 (2014)

    Google Scholar 

  9. De Leoni, M., van der Aalst, W.M.P.: Data-aware process mining: discovering decisions in processes using alignments. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 1454–1461 (2013)

    Google Scholar 

  10. Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061 (2020)

  11. van Dongen, B.F.: Dataset BPI Challenge 2019. 4TU. Centre for Research Data. https://doi.org/10.4121/uuid:d06aff4b-79f0-45e6-8ec8-e19730c248f1

  12. Kim, J., Ko, J., Lee, S.: Business Process Intelligence Challenge 2019: Process discovery and deviation analysis of purchase order handling process (2019)

    Google Scholar 

  13. BPI Challenge 2019. https://icpmconference.org/2019/icpm-2019/contests-challenges/bpi-challenge-2019

  14. Rinderle-Ma, S., Gall, M., Fdhila, W., Mangler, J., Indiono, C.: Collecting examples for instance-spanning constraints. arXiv preprint arXiv:1603.01523 (2016)

  15. Pufahl, L., Weske, M.: Batch activities in process modeling and execution. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 283–297. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45005-1_20

    Chapter  Google Scholar 

  16. Martin, N., Pufahl, L., Mannhardt, F.: Detection of batch activities from event logs. Inf. Syst. 95, 101642 (2021)

    Article  Google Scholar 

  17. Wen, Y., Chen, Z., Liu, J., Chen, J.: Mining batch processing workflow models from event logs. Concurr. Comput. Pract. Exp. 25(13), 1928–1942 (2013)

    Article  Google Scholar 

  18. Martin, N., Solti, A., Mendling, J., Depaire, B., Caris, A.: Mining batch activation rules from event logs. IEEE Trans. Serv. Comput. (2019). https://doi.org/10.1109/TSC.2019.2912163

  19. Senderovich, A., Di Francescomarino, C., Ghidini, C., Jorbina, K., Maggi, F.M.: Intra and inter-case features in predictive process monitoring: a tale of two dimensions. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 306–323. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65000-5_18

    Chapter  Google Scholar 

  20. van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)

    Article  Google Scholar 

  21. 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), pp. 25–32. (2020).

    Google Scholar 

  22. Outmazgin, N., Soffer, P.: A process mining-based analysis of business process work-arounds. Softw. Syst. Model. 15(2), 309–323 (2014). https://doi.org/10.1007/s10270-014-0420-6

    Article  Google Scholar 

  23. Rahmawati, D., Yaqin, M.A., Sarno, R.: Fraud detection on event logs of goods and services procurement business process using Heuristics Miner algorithm. In: 2016 International Conference on Information & Communication Technology and Systems (ICTS), pp. 249–254 (2016)

    Google Scholar 

  24. Weijters, A.J.M.M., van Der Aalst, W.M., De Medeiros, A.A.: Process mining with the heuristics miner-algorithm. Technical report, Technische Universiteit Eindhoven, WP, vol. 166, pp. 1–34 (2006)

    Google Scholar 

  25. Sarno, R., Dewandono, R.D., Ahmad, T., Naufal, M.F., Sinaga, F.: Hybrid association rule learning and process mining for fraud detection. IAENG Int. J. Comput. Sci. 42(2) (2015)

    Google Scholar 

Download references

Acknowledgement

The research was supported by the Israel Science Foundation under grant agreement 669/17.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Yael Dubinsky or Pnina Soffer .

Editor information

Editors and Affiliations

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

Dubinsky, Y., Soffer, P. (2021). Detecting the “Split-Cases” Workaround in Event Logs. In: Augusto, A., Gill, A., Nurcan, S., Reinhartz-Berger, I., Schmidt, R., Zdravkovic, J. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2021 2021. Lecture Notes in Business Information Processing, vol 421. Springer, Cham. https://doi.org/10.1007/978-3-030-79186-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-79186-5_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79185-8

  • Online ISBN: 978-3-030-79186-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics