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The Imprecisions of Precision Measures in Process Mining
In process mining, precision measures are used to quantify how much a pr...
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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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Anti-Alignments – Measuring The Precision of Process Models and Event Logs
Processes are a crucial artefact in organizations, since they coordinate...
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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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Implicit Recursive Characteristics of STOP
The most important notations of Communicating Sequential Process(CSP) ar...
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OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs
Providing appropriate structures around human resources can streamline o...
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Automated Discovery of Business Process Simulation Models from Event Logs
Simulation is a versatile technique for quantitative analysis of busines...
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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 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 measures have been developed throughout the years, but they are often defined in an ad-hoc manner without formally defining the desired properties up front. To address these problems, we formulate 21 conformance propositions and we use these propositions to evaluate current and existing conformance measures. The goal is to trigger a discussion by clearly formulating the challenges and requirements (rather than proposing new measures). Additionally, this paper serves as an overview of the conformance checking measures that are available in the process mining area.
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