A Predictive Approach Enabling Process Execution Recommendations

Schobel, Johannes and Reichert, Manfred (2017) A Predictive Approach Enabling Process Execution Recommendations. In: Advances in Intelligent Process-Aware Information Systems: Concepts, Methods, and Technologies. Intelligent Systems Reference Library, 123 . Springer, pp. 155-170. ISBN 978-3-319-52181-7

Full text not available from this repository.


In enterprises, decision makers need to continuously monitor business processes to guarantee for a high product and service quality. To accomplish this task, process-related data needs to be retrieved from various information systems - periodically or in real-time -- and then be aggregated based on key performance indicators (KPIs). If target values of the defined KPIs are violated (e.g., production takes longer than a predefined threshold), the reasons of these violations need to be identified. In general, such a retrospective analysis of business process data does not always contribute to prevent respective key performance violations. To remedy this drawback, process-aware information systems (PAIS) should enable the automated identification of processes, which are not well performing, and support users in executing these processes through recommendations. For example, it should be indicated, which problems might occur in future when taking the current course of the process instance as well as previous process instances into account. This chapter presents a methodology as well as an architecture for the support of predictive process analyses. In this context, algorithms from machine learning are applied to compare running process instances with historic process data and to identify diverging processes. In particular, the predictive approach will enable enterprises to quickly react to upcoming problems and inefficiencies.

Item Type: Book Section
Subjects: DBIS Research > Publications
Divisions: Faculty of Engineering, Electronics and Computer Science > Institute of Databases and Informations Systems > DBIS Research and Teaching > DBIS Research > Publications
Depositing User: M.Sc. Johannes Schobel
Date Deposited: 09 Jun 2017 09:11
Last Modified: 09 Jun 2017 09:11
URI: http://dbis.eprints.uni-ulm.de/id/eprint/1500

Actions (login required)

View Item
View Item