Alignment-based conformance checking for stochastic Petri nets

doi:10.1007/s13218-024-00879-0

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Alignment-based conformance checking for stochastic Petri nets

2025-03-07

PRE

Matheus Pereira de Almeida

Karina Valdivia Delgado

Sarajane Marques Peres

Marcelo Fantinato *

DOI：10.1007/s13218-024-00879-0

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Abstract

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The predominant focus of research in conformance checking does not account for the likelihood of behaviors allowed by the process model. If multiple activities are enabled based on the current state of the process model, they are assumed to have equal probabilities of occurring. However, some could be more probable than others. Existing approaches that find alignments using a stochastic model do not use a cost function directly and do not take into consideration log moves. Building on the existing alignment-based conformance-checking fundamentals, we formally define in this paper the alignment task that takes as input a stochastic Petri net and an event log trace, allows the use of log moves, and aims to produce an optimal ranking of alignments considering an optimization metric that combines both the firing probability of transitions in the stochastic Petri net and the cost of the alignments. We also propose ProbPlanAlign, the first approach that finds an optimal ranking of alignments that meets the reality of the trace occurrences in the event log using off-the-shelf probabilistic planners. An experiment with a real-life event log showed that ProbPlanAlign provides insights not found in existing probabilistic approaches. To analyze its scalability, an evaluation with synthetic event logs and process models with different numbers of transitions was also performed. ProbPlanAlign's time consumption to find an optimal ranking of alignments grows linearly when the size of the probabilistic Petri nets grows in terms of transitions. Specifically, the time consumption was 21.4 s for the larger process model evaluated, which has 237 transitions.

Keywords:

Process mining

Conformance checking

Alignment

Automated planning

Probabilistic planning