A framework to evaluate and compare decision-mining techniques

Toon Jouck, Massimiliano de Leoni, Benoît Depaire

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


    During the last decade several decision mining techniques have been developed to discover the decision perspective of a process from an event log. The increasing number of decision mining techniques raises the importance of evaluating the quality of the discovered decision models and/or decision logic. Currently, the evaluations are limited because of the small amount of available event logs with decision information. To alleviate this limitation, this paper introduces the ‘DataExtend’ technique that allows evaluating and comparing decision-mining techniques with each other, using a sufficient number of event logs and process models to generate evaluation results that are statistically significant. This paper also reports on an initial evaluation using ‘DataExtend’ that involves two techniques to discover decisions, whose results illustrate that the approach can serve the purpose.

    Original languageEnglish
    Title of host publicationBusiness Process Management Workshops - BPM 2018 International Workshops, Revised Papers
    EditorsFlorian Daniel, Quan Z. Sheng, Hamid Motahari
    Place of PublicationCham
    Number of pages12
    ISBN (Electronic)978-3-030-11641-5
    ISBN (Print)978-3-030-11640-8
    Publication statusPublished - 29 Jan 2019
    Event16th International Conference on Business Process Management, BPM 2018 - Sydney, Australia
    Duration: 9 Sep 201814 Sep 2018

    Publication series

    NameLecture Notes in Business Information Processing
    ISSN (Print)1865-1348


    Conference16th International Conference on Business Process Management, BPM 2018


    • Decision mining
    • Evaluation
    • Log generation

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