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Process Mining for Unstructured Data: Challenges and Research Directions

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dc.contributor.authorKoschmider, Agnes
dc.contributor.authorAleknonytė-Resch, Milda
dc.contributor.authorFonger, Frederik
dc.contributor.authorImenkamp, Christian
dc.contributor.authorLepsien, Arvid
dc.contributor.authorApaydin, Kaan
dc.contributor.authorJanssen, Dominik
dc.contributor.authorLanghammer, Dominic
dc.contributor.authorZiolkowski, Tobias
dc.contributor.authorZisgen, Yorck
dc.contributor.editorMichael, Judith
dc.contributor.editorWeske, Mathias
dc.date.accessioned2024-02-19T11:27:56Z
dc.date.available2024-02-19T11:27:56Z
dc.date.issued2024
dc.description.abstractThe application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format. To efficiently analyze unstructured data by process mining and to convey confidence into the analysis result, requires bridging multiple challenges. The purpose of this paper is to discuss these challenges, present initial solutions and describe future research directions. We hope that this article lays the foundations for future collaboration on this topic.en
dc.identifier.doi10.18420/modellierung2024_012
dc.identifier.isbn978-3-88579-742-5
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43614
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofModellierung 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-348
dc.subjectProcess Mining
dc.subjectUnstructured Data
dc.subjectChallenges
dc.subjectDirections
dc.titleProcess Mining for Unstructured Data: Challenges and Research Directionsen
dc.typeText/Conference Paper
gi.citation.endPage136
gi.citation.publisherPlaceBonn
gi.citation.startPage119
gi.conference.date12.-15. March 2024
gi.conference.locationPotsdam, Germany
gi.conference.sessiontitleBusiness Processes

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P348 - Modellierung 2024
Modellierung 2024 (LNI P348)