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Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)
Process mining sheds new light on the relationship between process model...
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Entropia: A Family of Entropy-Based Conformance Checking Measures for Process Mining
This paper presents a command-line tool, called Entropia, that implement...
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Adversarial System Variant Approximation to Quantify Process Model Generalization
In process mining, process models are extracted from event logs using pr...
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Towards Quantifying Privacy in Process Mining
Process mining employs event logs to provide insights into the actual pr...
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Log-based Evaluation of Label Splits for Process Models
Process mining techniques aim to extract insights in processes from even...
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An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining
Given an event log as a collection of recorded real-world process traces...
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Conformance Checking for a Medical Training Process Using Petri net Simulation and Sequence Alignment
Process Mining has recently gained popularity in healthcare due to its p...
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The Imprecisions of Precision Measures in Process Mining
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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