TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Heber, Eberhard
A1  - Hagen, Holger
A1  - Schmollinger, Martin
ED  - Zimmermann, Alfred
ED  - Rossmann, Alexander
T1  - Application of process mining for improving adaptivity in case management systems
T2  - Digital Enterprise Computing (DEC 2015) : June 25-26, 2015, Böblingen, Germany
N2  - 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.
KW  - adaptive case management
KW  - process mining
KW  - business process management
Y1  - 2015
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-6136
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-6136
UR  - http://subs.emis.de/LNI/Proceedings/Proceedings244/article13.html
SN  - 978-3-88579-638-1
SB  - 978-3-88579-638-1
SP  - 221
EP  - 231
S1  - 11
PB  - Gesellschaft für Informatik
CY  - Bonn
ER  -