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.

Maggi, F., Montali, M., Penaloza, N., Alman, A. (2020). Extending temporal business constraints with uncertainty. In Proceedings of the 18th International Conference on Business Process Management (BPM) (pp.35-54). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-58666-9_3].

Extending temporal business constraints with uncertainty

Penaloza Nyssen;
2020

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.
No
paper
Scientifica
Declarative process models; Probabilistic conformance checking; Probabilistic process monitoring; Process mining; Temporal logics
English
18th International Conference on Business Process Management, BPM 2020
9783030586652
Maggi, F., Montali, M., Penaloza, N., Alman, A. (2020). Extending temporal business constraints with uncertainty. In Proceedings of the 18th International Conference on Business Process Management (BPM) (pp.35-54). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-58666-9_3].
Maggi, F; Montali, M; Penaloza, N; Alman, A
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/299175
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