Confidence-driven communication of process mining on time series

dc.contributor.author	Koschmider, Agnes
dc.contributor.author	Oppelt, Natascha
dc.contributor.author	Hundsdörfer, Marie
dc.date.accessioned	2022-09-09T12:40:19Z
dc.date.available	2022-09-09T12:40:19Z
dc.date.issued	2022
dc.description.abstract	The combination of machine learning techniques with process analytics like process mining might even significantly elevate novel insights into time series data collections. To efficiently analyze time series by process mining and to convey confidence into the analysis result, requires bridging challenges. The purpose of this article is to discuss these challenges and to present initial solutions.	de
dc.identifier.doi	10.1007/s00287-022-01470-3
dc.identifier.pissn	1432-122X
dc.identifier.uri	http://dx.doi.org/10.1007/s00287-022-01470-3
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/39369
dc.publisher	Springer
dc.relation.ispartof	Informatik Spektrum: Vol. 45, No. 4
dc.relation.ispartofseries	Informatik Spektrum
dc.title	Confidence-driven communication of process mining on time series	de
dc.type	Text/Journal Article
gi.citation.endPage	228
gi.citation.startPage	223

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