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Mining Industrial Logs for System Level Insights

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dc.contributor.authorCzora, Sebastian
dc.contributor.authorDix, Marcel
dc.contributor.authorFromm, Hansjörg
dc.contributor.authorKlöpper, Benjamin
dc.contributor.authorSchmitz, Björn
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorNicklas, Daniela
dc.contributor.editorLeymann, Frank
dc.contributor.editorSchöning, Harald
dc.contributor.editorHerschel, Melanie
dc.contributor.editorTeubner, Jens
dc.contributor.editorHärder, Theo
dc.contributor.editorKopp, Oliver
dc.contributor.editorWieland, Matthias
dc.date.accessioned2017-06-21T11:24:44Z
dc.date.available2017-06-21T11:24:44Z
dc.date.issued2017
dc.description.abstractIndustrial systems are becoming more and more complex and expensive to operate. Companies are making considerable efforts to increase operational efficiency and eliminate unplanned downtime of their equipment. Condition monitoring has been applied to improve equipment availability and reliability. Most of the condition monitoring applications, however, focus on single components, not on entire systems. The objective of this research was to demonstrate that a combination of visual analytics and association rule mining can be successfully used in a condition monitoring context on system level.en
dc.identifier.isbn978-3-88579-660-2
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-266
dc.subjectcondition monitoring
dc.subjectpredictive maintenance
dc.subjectlog file analysis
dc.subjectdata mining
dc.titleMining Industrial Logs for System Level Insightsen
dc.typeText/Conference Paper
gi.citation.endPage64
gi.citation.publisherPlaceBonn
gi.citation.startPage57
gi.conference.date6.-10. März 2017
gi.conference.locationStuttgart
gi.conference.sessiontitleWorkshop Big Data Management Systems in Business and Industrial Applications (BigBIA17)

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P266 - BTW2017 - Datenbanksysteme für Business, Technologie und Web - Workshopband