Abstract
Temporal business constraints have been extensively adopted to declaratively capture the acceptable courses of execution in a business process. However, traditionally, constraints are interpreted logically in a crisp way: a process execution trace conforms with a constraint model if all the constraints therein are satisfied. This is too restrictive when one wants to capture best practices, constraints involving uncontrollable activities, and exceptional but still conforming behaviors. This calls for the extension of business constraints with uncertainty. In this paper, we tackle this timely and important challenge, relying on recent results on probabilistic temporal logics over finite traces. Specifically, our contribution is threefold. First, we delve into the conceptual meaning of probabilistic constraints and their semantics. Second, we argue that probabilistic constraints can be discovered from event data using existing techniques for declarative process discovery. Third, we study how to monitor probabilistic constraints, where constraints and their combinations may be in multiple monitoring states at the same time, though with different probabilities.
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Notes
- 1.
Recall that a \(\mathsf {response}\) constraint is satisfied if every execution of the source is followed by the execution of the target.
- 2.
Implausible scenarios are irrelevant: they produce an output that is associated to probability 0.
- 3.
- 4.
- 5.
In the screenshots, 1 and 2 represent the probabilistic constraints labeled with 1 and 2 in Fig. 2, whereas 3 represents the crisp constraint in the same example.
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This work has been supported by the Estonian Research Council (project PRG887).
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Maggi, F.M., Montali, M., Peñaloza, R., Alman, A. (2020). Extending Temporal Business Constraints with Uncertainty. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process Management. BPM 2020. Lecture Notes in Computer Science(), vol 12168. Springer, Cham. https://doi.org/10.1007/978-3-030-58666-9_3
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