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dc.contributor.authorMannhardt, Felix
dc.contributor.authorBovo, Riccardo
dc.contributor.authorOliveira, Manuel Fradinho
dc.contributor.authorJulier, Simon
dc.date.accessioned2018-12-06T07:12:07Z
dc.date.available2018-12-06T07:12:07Z
dc.date.created2018-12-05T11:55:02Z
dc.date.issued2018
dc.identifier.citationLecture Notes in Computer Science. 2018, 11315 84-93.nb_NO
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11250/2576243
dc.description.abstractDespite the increasing automation levels in an Industry 4.0 scenario, the tacit knowledge of highly skilled manufacturing workers remains of strategic importance. Retaining this knowledge by formally capturing it is a challenge for industrial organisations. This paper explores research on automatically capturing this knowledge by using methods from activity recognition and process mining on data obtained from sensorised workers and environments. Activity recognition lifts the abstraction level of sensor data to recognizable activities and process mining methods discover models of process executions. We classify the existing work, which largely neglects the possibility of applying process mining, and derive a taxonomy that identifies challenges and research gaps.
dc.description.abstractA Taxonomy for Combining Activity Recognition and Process Discovery in Industrial Environments
dc.language.isoengnb_NO
dc.titleA Taxonomy for Combining Activity Recognition and Process Discovery in Industrial Environmentsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersion
dc.source.pagenumber84-93nb_NO
dc.source.volume11315nb_NO
dc.source.journalLecture Notes in Computer Sciencenb_NO
dc.identifier.doi10.1007/978-3-030-03496-2_10
dc.identifier.cristin1639391
cristin.unitcode7401,90,30,0
cristin.unitnameTeknologiledelse
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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