Efficient and fast monitoring and disruption management for a pressure diecast system

dc.contributor.author	Hegenbarth, Yvonne
dc.contributor.author	Bartsch, Thomas
dc.contributor.author	Ristow, Gerald H.
dc.date.accessioned	2021-06-21T10:10:38Z
dc.date.available	2021-06-21T10:10:38Z
dc.date.issued	2018
dc.description.abstract	An 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.identifier.doi	10.1515/itit-2017-0039
dc.identifier.pissn	2196-7032
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/36609
dc.language.iso	en
dc.publisher	De Gruyter
dc.relation.ispartof	it - Information Technology: Vol. 60, No. 3
dc.subject	Big Data
dc.subject	Streaming Analytics
dc.subject	Predictive Maintenance
dc.subject	Industry 4.0
dc.subject	Internet of Things
dc.subject	Incident
dc.subject	Reaction and Disruption Management
dc.title	Efficient and fast monitoring and disruption management for a pressure diecast system	en
dc.type	Text/Journal Article
gi.citation.endPage	171
gi.citation.publisherPlace	Berlin
gi.citation.startPage	165

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