Probabilistic Trace Alignment
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Abstract (arXiv)
Alignments provide sophisticated diagnostics that pinpoint deviations in a trace with respect to a process model and their severity. However, approaches based on trace alignments use crisp process models as reference and recent probabilistic conformance checking approaches check the degree of conformance of an event log with respect to a stochastic process model instead of finding trace alignments. In this paper, for the first time, we provide a conformance checking approach based on trace alignments using stochastic Workflow nets. Conceptually, this requires to handle the two possibly contrasting forces of the cost of the alignment on the one hand and the likelihood of the model trace with respect to which the alignment is computed on the other.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jul 2021.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 2.
6 papers.
- 2024Conformance Checking of Fuzzy Logs against Declarative Temporal Specificationscited by 5
- 2022Conformance Checking with Uncertainty via SMT (Extended Version)cited by 4
- 2023Enjoy the Silence: Analysis of Stochastic Petri Nets with Silent Transitionscited by 1
- 2022Alignment-based conformance checking over probabilistic eventscited by 0
- 2025Towards Explainable Sequential Learningcited by 0
- 2025Stochastic Alignments: Matching an Observed Trace to Stochastic Process Modelscited by 0
- 2025Stochastic Alignments: Matching an Observed Trace to Stochastic Process Models9 Jul 2025
- 2025Towards Explainable Sequential Learning29 May 2025
- 2024Conformance Checking of Fuzzy Logs against Declarative Temporal Specifications17 Jun 2024
- 2023Enjoy the Silence: Analysis of Stochastic Petri Nets with Silent Transitions10 Jun 2023
- 2022Alignment-based conformance checking over probabilistic events9 Sep 2022
- 2022Conformance Checking with Uncertainty via SMT (Extended Version)15 Jun 2022