Gesellschaft für Informatik e.V.

Lecture Notes in Informatics


Digital Enterprise Computing (DEC 2015) P-244, 221-231 (2015).

Gesellschaft für Informatik, Bonn
2015


Copyright © Gesellschaft für Informatik, Bonn

Contents

Application of process mining for improving adaptivity in case management systems

Eberhard Heber , Holger Hagen and Martin Schmollinger

Abstract


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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Gesellschaft für Informatik, Bonn
ISBN 978-3-88579-638-1


Last changed 06.11.2015 19:13:55