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.
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
Alter, S.: Theory of Workarounds. Commun. Assoc. Inf. Syst. 34, 1041–1066 (2014)
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
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)
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
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
Winter, K., Stertz, F., Rinderle-Ma, S.: Discovering instance and process spanning constraints from process execution logs. Inf. Syst. 89, 101484 (2020)
Van Der Aalst, W.M.P.: Process mining: overview and opportunities. ACM Trans. Manag. Inf. Syst. (TMIS) 3(2), 1–17 (2012)
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)
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)
Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061 (2020)
van Dongen, B.F.: Dataset BPI Challenge 2019. 4TU. Centre for Research Data. https://doi.org/10.4121/uuid:d06aff4b-79f0-45e6-8ec8-e19730c248f1
Kim, J., Ko, J., Lee, S.: Business Process Intelligence Challenge 2019: Process discovery and deviation analysis of purchase order handling process (2019)
BPI Challenge 2019. https://icpmconference.org/2019/icpm-2019/contests-challenges/bpi-challenge-2019
Rinderle-Ma, S., Gall, M., Fdhila, W., Mangler, J., Indiono, C.: Collecting examples for instance-spanning constraints. arXiv preprint arXiv:1603.01523 (2016)
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
Martin, N., Pufahl, L., Mannhardt, F.: Detection of batch activities from event logs. Inf. Syst. 95, 101642 (2021)
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)
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
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
van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)
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).
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
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)
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)
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)
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The research was supported by the Israel Science Foundation under grant agreement 669/17.
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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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