Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/36703
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dc.contributor.authorVAN HOUDT, Greg-
dc.contributor.authorDEPAIRE, Benoit-
dc.contributor.authorMARTIN, Niels-
dc.date.accessioned2022-02-22T12:28:37Z-
dc.date.available2022-02-22T12:28:37Z-
dc.date.issued2022-
dc.date.submitted2022-02-10T16:31:23Z-
dc.identifier.citationJorge Munoz-Gama, Xixi Lu (Ed.), Process Mining Workshops ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers, Springer, p.73-84-
dc.identifier.isbn9783030985806-
dc.identifier.isbn9783030985813-
dc.identifier.issn1865-1348-
dc.identifier.issn1865-1356-
dc.identifier.urihttp://hdl.handle.net/1942/36703-
dc.description.abstractProcess mining is a research domain that enables businesses to analyse and improve their processes by extracting insights from event logs. While determining the root causes of, for example, a negative case outcome can provide valuable insights for business users, only limited research has been conducted to uncover true causal relations within the process mining field. Therefore, this paper proposes AITIA-PM, a novel technique to measure cause-effect relations in event logs based on causality theory. The AITIA-PM algorithm employs probabilistic temporal logic to formally yet flexibly define hypotheses and then automatically tests them for causal relations from data. We demonstrate this by applying AITIA-PM on a real-life dataset. The case study shows that, after a well-thought-out hypotheses definition and information extraction, the AITIA-PM algorithm can be applied on rich event logs, expanding the possibilities of meaningful root cause analysis in a process mining context.-
dc.language.isoen-
dc.publisherSpringer-
dc.relation.ispartofseriesLecture Notes in Business Information Processing-
dc.subject.otherProcess Mining-
dc.subject.otherRoot Cause Analysis-
dc.subject.otherProbabilistic Temporal Logic-
dc.subject.otherEvent Log-
dc.titleRoot Cause Analysis in Process Mining with Probabilistic Temporal Logic-
dc.typeProceedings Paper-
local.bibliographicCitation.authorsJorge Munoz-Gama, Xixi Lu-
local.bibliographicCitation.conferencedate31/10/2021-04/11/2021-
local.bibliographicCitation.conferencename3rd International Conference on Process Mining (ICPM 2021)-
local.bibliographicCitation.conferenceplaceEindhoven, The Netherlands-
dc.identifier.epage84-
dc.identifier.spage73-
dc.identifier.volume433-
local.bibliographicCitation.jcatC1-
local.publisher.placeGEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
local.relation.ispartofseriesnr433-
dc.identifier.doi10.1007/978-3-030-98581-3_6-
dc.identifier.isi000787744500006-
local.provider.typePdf-
local.bibliographicCitation.btitleProcess Mining Workshops ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers-
local.uhasselt.uhpubyes-
local.uhasselt.internationalno-
item.accessRightsOpen Access-
item.fulltextWith Fulltext-
item.fullcitationVAN HOUDT, Greg; DEPAIRE, Benoit & MARTIN, Niels (2022) Root Cause Analysis in Process Mining with Probabilistic Temporal Logic. In: Jorge Munoz-Gama, Xixi Lu (Ed.), Process Mining Workshops ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers, Springer, p.73-84.-
item.contributorVAN HOUDT, Greg-
item.contributorDEPAIRE, Benoit-
item.contributorMARTIN, Niels-
Appears in Collections:Research publications
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