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An Alignment Cost-Based Classification of Log Traces Using Machine-Learning

Mathilde Boltenhagen 1, 2 Benjamin Chetioui 3 Laurine Huber 4
1 MEXICO - Modeling and Exploitation of Interaction and Concurrency
Inria Saclay - Ile de France, LSV - Laboratoire Spécification et Vérification
4 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Conformance checking is an important aspect of process mining that identifies the differences between the behaviors recorded in a log and those exhibited by an associated process model. Machine learning and deep learning methods perform extremely well in sequence analysis. We successfully apply both a Recurrent Neural Network and a Random Forest classifiers to the problem of evaluating whether the alignment cost of a log trace to a process model is below an arbitrary threshold, and provide a lower bound for the fitness of the process model based on the classification.
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https://hal.inria.fr/hal-03134114
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Submitted on : Monday, February 8, 2021 - 9:53:24 AM
Last modification on : Friday, January 21, 2022 - 3:11:23 AM
Long-term archiving on: : Sunday, May 9, 2021 - 6:20:03 PM

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Mathilde Boltenhagen, Benjamin Chetioui, Laurine Huber. An Alignment Cost-Based Classification of Log Traces Using Machine-Learning. ML4PM2020 - First International Workshop on Leveraging Machine Learning in Process Mining, Oct 2020, Padua/ Virtual, Italy. ⟨10.1007/978-3-030-72693-5_11⟩. ⟨hal-03134114⟩

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