The imprecisions of precision measures in process mining

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalInformation Processing Letters
Volume135
DOIs
Publication statusPublished - 14 Feb 2018

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Process Mining
Imprecision
Quantify
Axioms
Process Model
Counterexample
Approximation
Model

Keywords

  • Design of algorithms
  • Formal languages and automata
  • Petri nets
  • Process mining

Cite this

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title = "The imprecisions of precision measures in process mining",
abstract = "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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author = "N. Tax and X. Lu and N. Sidorova and D. Fahland and {van der Aalst}, W.M.P.",
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The imprecisions of precision measures in process mining. / Tax, N.; Lu, X.; Sidorova, N.; Fahland, D.; van der Aalst, W.M.P.

In: Information Processing Letters, Vol. 135, 14.02.2018, p. 1-8.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - The imprecisions of precision measures in process mining

AU - Tax, N.

AU - Lu, X.

AU - Sidorova, N.

AU - Fahland, D.

AU - van der Aalst, W.M.P.

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AB - 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.

KW - Design of algorithms

KW - Formal languages and automata

KW - Petri nets

KW - Process mining

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