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

Abstract

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
PublisherSpringer
Pages482-493
Number of pages12
ISBN (Electronic)978-3-030-11641-5
ISBN (Print)978-3-030-11640-8
DOIs
Publication statusPublished - 29 Jan 2019
Event16th International Conference on Business Process Management (BPM 2018) - Sydney, Australia
Duration: 9 Sep 201814 Sep 2018
Conference number: 16
http://ceur-ws.org/Vol-2196/

Publication series

NameLecture Notes in Business Information Processing
Volume342
ISSN (Print)1865-1348

Conference

Conference16th International Conference on Business Process Management (BPM 2018)
Abbreviated titleBPM 2018
Country/TerritoryAustralia
CitySydney
Period9/09/1814/09/18
OtherDissertation Award, Demonstration, and Industrial Track at BPM
Internet address

Keywords

  • Decision mining
  • Evaluation
  • Log generation

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