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Analyzing business process changes using influence analysis

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Lehto, Teemu
dc.contributor.author Hinkka, Markku
dc.contributor.author Hollmén, Jaakko
dc.date.accessioned 2019-01-14T09:25:22Z
dc.date.available 2019-01-14T09:25:22Z
dc.date.issued 2018-01-01
dc.identifier.citation Lehto , T , Hinkka , M & Hollmén , J 2018 , ' Analyzing business process changes using influence analysis ' , CEUR Workshop Proceedings , vol. 2270 , pp. 32-46 . en
dc.identifier.issn 1613-0073
dc.identifier.other PURE UUID: eecd3731-4978-4e44-8a38-54bc83befb0f
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/eecd3731-4978-4e44-8a38-54bc83befb0f
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85058821100&partnerID=8YFLogxK
dc.identifier.other PURE LINK: http://ceur-ws.org/Vol-2270/
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/30820210/paper3.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/36041
dc.description.abstract Real world business operations are continuously changing. Periodical business performance review sessions typically focus on monitoring changes in key performance indicator (KPI) measures. However, the detection and review of activity level changes in actual business processes is often based on subjective manual observations. This means that many changes are not detected in timely manner making the organization slower to adapt to changes. In this paper we present a systematic method for detecting business process changes for business review purposes based on transaction level data. Our method uses process mining principles and is based on our previously published influence analysis methodology. Unlike most process mining change detection algorithms which operate on case level our method analyzes changes in the individual event level. We show how case level data can be used to construct features to the event level. Our method detects changes in timely manner since there is no need to wait for the cases to be completed. We present two alternative ways, binary approach and continuous event-age approach, for dividing events into recent and old for business review purpose. We also demonstrate the method with data from a real-life case. en
dc.format.extent 15
dc.format.extent 32-46
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries CEUR Workshop Proceedings en
dc.relation.ispartofseries Volume 2270 en
dc.rights openAccess en
dc.title Analyzing business process changes using influence analysis en
dc.type Conference article fi
dc.description.version Peer reviewed en
dc.contributor.department School services,SCI
dc.contributor.department Helsinki Institute for Information Technology (HIIT)
dc.contributor.department Department of Computer Science en
dc.subject.keyword Change detection
dc.subject.keyword Concept drift
dc.subject.keyword Contribution
dc.subject.keyword Data mining
dc.subject.keyword Influence analysis
dc.subject.keyword Key performance indicator
dc.subject.keyword Performance management
dc.subject.keyword Process analysis
dc.subject.keyword Process improvement
dc.subject.keyword Process mining
dc.subject.keyword Root cause analysis
dc.identifier.urn URN:NBN:fi:aalto-201901141224
dc.type.version publishedVersion

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