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Application of process mining for improving adaptivity in case management systems

  • The character of knowledge-intense processes is that participants decide the next process activities on base of the present information and their expert knowledge. The decisions of these knowledge workers are in general non-deterministic. It is not possible to model these processes in advance and to automate them using a process engine of a BPM system. Hence, in this context a process instance is called a case, because there is no predefined model that could be instantiated. Domain-specific or general case management systems are used to support the knowledge workers. These systems provide all case information and enable users to define the next activities, but they have no or only limited activity recommendation capabilities. In the following paper, we present a general concept for a self-learning system based on process mining that suggests the next best activity on quantitative and qualitative data for a given case. As a proof of concept, it was applied to the area of insurance claims settlement.

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Metadaten
Name:Schmollinger, Martin
URN:urn:nbn:de:bsz:rt2-opus4-6136
URL:http://subs.emis.de/LNI/Proceedings/Proceedings244/article13.html
ISBN:978-3-88579-638-1
Erschienen in:Digital Enterprise Computing (DEC 2015) : June 25-26, 2015, Böblingen, Germany
Publisher:Gesellschaft für Informatik
Place of publication:Bonn
Editor:Alfred Zimmermann, Alexander Rossmann
Document Type:Conference Proceeding
Language:English
Year of Publication:2015
Tag:adaptive case management; business process management; process mining
Pagenumber:11
First Page:221
Last Page:231
Catalogue entry:Im Katalog der Hochschule Reutlingen ansehen
Dewey Decimal Classification:004 Informatik
Open Access:Ja
Licence (German):License Logo  Open Access