Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)
Anja F. Syring, Niek Tax, Wil M.P. van der Aalst

TL;DR
This paper introduces 21 formal conformance propositions to evaluate existing process mining conformance measures, aiming to clarify their properties and foster discussion on their effectiveness.
Contribution
It formulates a comprehensive set of conformance propositions and uses them to systematically evaluate current conformance measures in process mining.
Findings
Identifies gaps and inconsistencies in existing measures
Provides a structured evaluation framework for conformance measures
Stimulates discussion on formal properties of conformance metrics
Abstract
Process mining sheds new light on the relationship between process models and real-life processes. Process discovery can be used to learn process models from event logs. Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the execution of the business process. There exist different categories of conformance measures. Recall, also called fitness, is concerned with quantifying how much of the behavior that was observed in the event log fits the process model. Precision is concerned with quantifying how much behavior a process model allows for that was never observed in the event log. Generalization is concerned with quantifying how well a process model generalizes to behavior that is possible in the business process but was never observed in the event log. Many recall, precision, and generalization…
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Service-Oriented Architecture and Web Services · Semantic Web and Ontologies
