Abstract
Modelling processes with declarative process models, i.e. sets of constraints, allows for a great degree of flexibility in process execution. However, having behavior specified by means of symbolic (textual) constraints comes along with the problem that it is often hard for humans to understand which exact behavior is allowed, and which is not (think for example of checking relationships between constraints). This becomes especially problematic when modellers need to carry out changes to a model. For example, a modeller must make sure that any alteration to a model does not introduce any unwanted or non-compliant behavior. As this is often difficult for humans, editing declarative process models currently bears the risk of (accidentally) inducing unforeseen compliance breaches due to some overlooked changes in behavior. In this work, we therefore present an approach to efficiently compute the behavioral changes between a declarative process model M and a corresponding (edited) model \(M'\). This supports modellers in understanding the behavioral changes induced by an alteration to the constraints. We implement our approach and show that behavioral changes can be computed within milliseconds even for real-life data-sets.
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Notes
- 1.
Note that we assume both D and \(D'\) to be satisfiable.
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Acknowledgements
We thank Gaël Bernard and colleagues for help and access to their tool for selecting representative sample traces [3].
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Schützenmeier, N., Corea, C., Delfmann, P., Jablonski, S. (2023). Efficient Computation of Behavioral Changes in Declarative Process Models. In: van der Aa, H., Bork, D., Proper, H.A., Schmidt, R. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2023 2023. Lecture Notes in Business Information Processing, vol 479. Springer, Cham. https://doi.org/10.1007/978-3-031-34241-7_10
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