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Detecting the “Split-Cases” Workaround in Event Logs

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Enterprise, Business-Process and Information Systems Modeling (BPMDS 2021, EMMSAD 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 421))

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.

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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 

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Acknowledgement

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

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Authors and Affiliations

  1. University of Haifa, Mount Carmel, 3498838, Haifa, Israel

    Yael Dubinsky & Pnina Soffer

Authors
  1. Yael Dubinsky
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  2. Pnina Soffer
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Corresponding authors

Correspondence to Yael Dubinsky or Pnina Soffer .

Editor information

Editors and Affiliations

  1. University of Melbourne, Parkville, VIC, Australia

    Adriano Augusto

  2. University of Technology Sydney, Ultimo, NSW, Australia

    Asif Gill

  3. University Paris 1 Panthéon-Sorbonne, Paris, France

    Selmin Nurcan

  4. University of Haifa, Haifa, Israel

    Iris Reinhartz-Berger

  5. Munich University of Applied Sciences, Munich, Germany

    Rainer Schmidt

  6. Stockholm University, Kista, Sweden

    Jelena Zdravkovic

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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

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79185-8

  • Online ISBN: 978-3-030-79186-5

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