Abstract
It is common for the observed behavior of a business process to differ from the behavior captured in its corresponding model, as workers devise workarounds to handle special circumstances, which over time become part of the norm. Process model repair methods help modelers to realign their models with the observed behavior as recorded in an event log. Given a process model and an event log, these methods produce a new process model that more closely matches the log, while resembling the original model as close as possible. Existing repair methods identify points in the process where the log deviates from the model, and fix these deviations by adding behavior to the model locally. In their quest for automation, these methods often add too much behavior to the model, resulting in models that over-generalize the behavior in the log. This paper advocates for an interactive and incremental approach to process model repair, where differences between the model and the log are visually displayed to the user, and the user repairs each difference manually based on the provided visual guidance. An empirical evaluation shows that the proposed method leads to repaired models that avoid the over-generalization pitfall of state-of-the-art automated repair methods.
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Notes
- 1.
Both methods produce Petri nets but for simplicity we present the repaired models in BPMN.
- 2.
The same rationale of reconciling changes with higher impact first is proposed in [21].
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Available at http://www.apromore.org.
- 4.
A screencast of the tool can be found at https://youtu.be/3d00pORc9X8.
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- 6.
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Acknowledgments
We thank Artem Polyvyanyy and Raffaele Conforti for their feedback on earlier versions of this work. This research is funded by the Australian Research Council (grant DP150103356) and the Estonian Research Council (grant IUT20-55).
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Armas Cervantes, A., van Beest, N.R.T.P., La Rosa, M., Dumas, M., García-Bañuelos, L. (2017). Interactive and Incremental Business Process Model Repair. In: Panetto, H., et al. On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science(), vol 10573. Springer, Cham. https://doi.org/10.1007/978-3-319-69462-7_5
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