The Imprecisions of Precision Measures in Process Mining

05/03/2017 ∙ by Niek Tax, et al. ∙ 0 ∙ share

In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been proposed throughout the years, no research has been done to validate whether these measures achieve the intended aim of quantifying over-approximation in a consistent way for all models and logs. This paper fills this gap by postulating a number of axioms for quantifying precision consistently for any log and any model. Further, we show through counter-examples that none of the existing measures consistently quantifies precision.

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The Imprecisions of Precision Measures in Process Mining

Authors

Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)

Entropia: A Family of Entropy-Based Conformance Checking Measures for Process Mining

Adversarial System Variant Approximation to Quantify Process Model Generalization

Towards Quantifying Privacy in Process Mining

Log-based Evaluation of Label Splits for Process Models

An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining

Conformance Checking for a Medical Training Process Using Petri net Simulation and Sequence Alignment

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