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dc.contributor.authorHegenbarth, Yvonne
dc.contributor.authorBartsch, Thomas
dc.contributor.authorRistow, Gerald H.
dc.date.accessioned2021-06-21T10:10:38Z
dc.date.available2021-06-21T10:10:38Z
dc.date.issued2018
dc.identifier.issn2196-7032
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/36609
dc.description.abstractAn increasing amount of information is collected in industrial production processes. In many cases, this data is only accessible to the vendor of the machines involved in the production process. In the government-funded research project BigPro, we propose a flexible and fast Big Data platform that allows detection and reaction to incidents and anomalies in the production process in near real-time.en
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.ispartofit - Information Technology: Vol. 60, No. 3
dc.subjectBig Data
dc.subjectStreaming Analytics
dc.subjectPredictive Maintenance
dc.subjectIndustry 4.0
dc.subjectInternet of Things
dc.subjectIncident
dc.subjectReaction and Disruption Management
dc.titleEfficient and fast monitoring and disruption management for a pressure diecast systemen
dc.typeText/Journal Article
dc.pubPlaceBerlin
mci.reference.pages165-171
dc.identifier.doi10.1515/itit-2017-0039


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