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dc.contributor.authorHeber, Eberhard
dc.contributor.authorHagen, Holger
dc.contributor.authorSchmollinger, Martin
dc.contributor.editorZimmermann, Alfred
dc.contributor.editorRossmann, Alexander
dc.date.accessioned2017-06-30T08:22:13Z
dc.date.available2017-06-30T08:22:13Z
dc.date.issued2015
dc.identifier.isbn978-3-88579-638-1
dc.identifier.issn1617-5468
dc.description.abstractThe 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.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDigital Enterprise Computing (DEC 2015)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-244
dc.titleApplication of process mining for improving adaptivity in case management systemsen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages221-231
mci.conference.locationBöblingen
mci.conference.date25.-26. June 2015


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