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Detecting cross-case associations in an event log: toward a pattern-based detection

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Abstract

Business process management, design, and analysis is mostly centered around a process model, which depicts the behavior of a process case (instance). As a result, behavior that associates several cases together has received less attention. Yet, it is important to understand and track associations among cases, as they bear substantial consequences for compliance with regulations, root cause analysis of performance issues, exception handling, and prediction. This paper presents a framework of cross-case association patterns, categorized as intended association patterns and contextual association patterns. It further conceptualizes two example patterns—one for each category, and proposes techniques for detecting these patterns in an event log. The “split-case” workaround is an example of a pattern in the intended association category, and its proposed detection method exemplifies how patterns in this category can be approached. The patterns of a shared entity and a shared resource are contextual association patterns, which we propose to detect by means of hidden concept drifts. Evaluation of the two detection approaches is reported, using simulated logs for assessing their internal validity as well as real-life ones for exploring their external validity.

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Notes

  1. https://doi.org/10.4121/uuid:d06aff4b-79f0-45e6-8ec8-e19730c248f1.

  2. https://fluxicon.com/disco/.

  3. https://doi.org/10.4121/uuid:a7ce5c55-03a7-4583-b855-98b86e1a2b07.

  4. https://doi.org/10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f.

References

  1. Verbeek, H.M.W., Buijs, J.C.A.M., Dongen, B.F., Aalst, W.M.P.: XES, XESame, and ProM 6. Inform. Syst. Evolut. 72, 60–75 (2011)

  2. van der Aalst W.M.P. et al., (2011). Process mining manifesto. In International conference on business process management (pp. 169–194). Springer, Berlin, Heidelberg.‏

  3. Winter, K., Rinderle-Ma, S.: Discovering instance-spanning constraints from process execution logs based on classification techniques. In: Enterprise Distributed Object Computing Conference, pp. 79–88 (2017).

  4. Kannan, K.S., Manoj, K., Arumugam, S.: Labeling methods for identifying outliers. Int. J. Stat. Syst. 10(2), 231–238 (2015)

    Google Scholar 

  5. Grinvald, A., Soffer, P., & Mokryn, O.: Inter-case properties and process variant considerations in time prediction: A conceptual framework. In: Enterprise, Business-Process and Information Systems Modeling, pp. 96–111. Springer, Cham (2021).‏

  6. Dubinsky, Y., Soffer, P.: Detecting the “Split-Cases” Workaround in event logs. In: Enterprise, Business-Process and Information Systems Modeling, pp. 47–61. Springer, Cham (2021).‏

  7. Rinderle-Ma, S., Gall, M., Fdhila, W., Mangler, J., Indiono, C.: Collecting examples for instance-spanning constraints (2016). http://arxiv.org/abs/1603.01523

  8. Aamer, H., Montali, M., Bussche, J. V. D.: What can database query processing do for instance-spanning constraints?. BPM 2022 workshops (2022).‏

  9. 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 

  10. van der Aalst W.M.P., et al.: ProM: The process mining toolkit. BPM (Demos) 489(31), 2 (2009)

    Google Scholar 

  11. Outmazgin, N., Soffer, P.: A process mining-based analysis of business process work-arounds. Softw. Syst. Model. 15(2), 309–323 (2016)

    Article  Google Scholar 

  12. Carmona, J., van Dongen, B., Solti, A., Weidlich, M.: Conformance checking. Springer, Switzerland (2018)

    Book  Google Scholar 

  13. 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 (2013).

  14. Klijn, E. L., & Fahland, D. (2020). 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. IEEE, New York

  15. Kim, J., Jonghyeon K., Suhwan L.: Business process intelligence challenge 2019: Process discovery and deviation analysis of purchase order handling process.

  16. Martjushev, J., Bose, R. J. C., Van Der Aalst, W. M.: Change point detection and dealing with gradual and multi-order dynamics in process mining. In: Perspectives in Business Informatics Research: 14th International Conference, BIR 2015, Tartu, Estonia, August 26–28, 2015, Proceedings 14 (pp. 161–178). Springer International Publishing (2015).

  17. Fdhila, W., Gall, M., Rinderle-Ma, S., Mangler, J., Indiono, C.: Classification and formalization of instance-spanning constraints in process-driven applications. In: International Conference on Business Process Management, pp. 348–364. Springer, Cham (2016).‏

  18. Amin, R.: Handling instance spanning constraints in compliance management. ABC J. Adv. Res. 8(2), 95–108 (2019)

    Article  Google Scholar 

  19. Steinau, S., Andrews, K., Reichert, M.: The relational process structure. In International Conference on Advanced Information Systems Engineering, pp. 53–67. Springer, Cham (2018).‏

  20. 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 

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

    Article  Google Scholar 

  22. Waibel, P., Novak, C., Bala, S., Revoredo, K., Mendling, J.: Analysis of Business Process Batching Using Causal Event Models. In International Conference on Process Mining, pp. 17–29. Springer, Cham (2020).‏

  23. Klijn, E. L., Fahland, D.: Performance mining for batch processing using the performance spectrum. In: International Conference on Business Process Management, pp. 172–185. Springer, Cham (2019).‏

  24. Senderovich, A., Leemans, S. J., Harel, S., Gal, A., Mandelbaum, A., van der Aalst, W. M.: Discovering queues from event logs with varying levels of information. In: International Conference on Business Process Management, pp. 154–166. Springer, Cham (2016).‏

  25. 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 

  26. Arias, M., Rojas, E., Munoz-Gama, J., Sepúlveda, M.: A framework for recommending resource allocation based on process mining. In: International Conference on Business Process Management, pp. 458–470. Springer, Cham (2016)

  27. Pourbafrani, M., Kar, S., Kaiser, S., & van der Aalst, W. M. P.: Remaining time prediction for processes with inter-case dynamics. In: 2nd International Workshop on Leveraging Machine Learning in Process Mining ICPM (2021).‏

  28. Senderovich, A., Di Francescomarino, C., 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 

  29. Tsoury, A., Soffer, P., Reinhartz-Berger, I.: How well did it recover? Impact-aware conformance checking. Computing 103(1), 3–27 (2021)

    Article  MathSciNet  Google Scholar 

  30. Russell, N., Aalst, van der W.M.P., Hofstede, A. T.: Workflow exception patterns. In: International Conference on Advanced Information Systems Engineering, pp. 288–302. Springer, Berlin (2006).‏

  31. Ghahfarokhi, A. F., Park, G., Berti, A., van der Aalst, W.M.P.: OCEL: A standard for object-centric event logs. In: European Conference on Advances in Databases and Information Systems, pp. 169–175. Springer, Cham (2021).‏

  32. van der Aalst W.M.P.: Object-centric process mining: Dealing with divergence and convergence in event data. In International Conference on Software Engineering and Formal Methods, pp. 3–25. Springer, Cham (2019).‏

  33. van der Aalst W.M.P., Berti, A.: Discovering object-centric Petri nets. Fundamenta informaticae 175(1–4), 1–40 (2020)

    Article  MathSciNet  Google Scholar 

  34. Outmazgin, N., Soffer, P., Hadar, I.: Workarounds in business processes: A goal-based analysis. In: International Conference on Advanced Information Systems Engineering, pp. 368–383. Springer, Cham (2020)

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Information Systems, University of Hafa, Haifa, Israel

    Yael Dubinsky, Pnina Soffer & Irit Hadar

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  1. Yael Dubinsky
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  2. Pnina Soffer
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  3. Irit Hadar
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Correspondence to Pnina Soffer.

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Communicated by Selmin Nurcan, Rainer Schmidt, and Adriano Augusto.

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Dubinsky, Y., Soffer, P. & Hadar, I. Detecting cross-case associations in an event log: toward a pattern-based detection. Softw Syst Model 22, 1755–1777 (2023). https://doi.org/10.1007/s10270-023-01100-w

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