Abstract
Declarative model minimization is a computationally expensive task. State-of-the-art approximation techniques rely on hard-coded heuristic functions based on properties of constraint templates, which requires rework when new templates are added and cannot handle models expressed as arbitrary logical formulas. We present a precision-based heuristic function that requires no pre-configuration and can handle arbitrary constraints, provided they can be mapped to a finite automaton. The approach is evaluated on real-world datasets, where it outperforms state-of-the-art methods while accepting a wider range of inputs.
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Notes
- 1.
[6] introduces the Linear Dynamic Logic over Finite Traces (LDL\(_f\)), which is as expressive as DFAs. We do not discuss LDL\(_f\) formulas, but we notice that methods presented here work with arbitrary DFAs and hence are directly applicable to them.
- 2.
The datasets can be found at github.com/EduardoGoulart1/BPMDS24.
- 3.
Extracted from github.com/cdc08x/MINERful/commit/8311258.
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We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.
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Goulart Rocha, E., van der Aalst, W.M.P. (2024). Precision-Guided Minimization of Arbitrary Declarative Process Models. In: van der Aa, H., Bork, D., Schmidt, R., Sturm, A. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2024 2024. Lecture Notes in Business Information Processing, vol 511. Springer, Cham. https://doi.org/10.1007/978-3-031-61007-3_5
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