preCEP: Facilitating Predictive Event-Driven Process Analytics

Schwegmann Bernd, Matzner Martin, Janiesch Christian


Abstract
The earlier critical decision can be made, the more business value can be retained or even earned. The goal of this research is to reduce a decision maker's action distance to the observation of critical events. We report on the development of the software tool preCEP that facilitates predictive event-driven process analytics (edPA). The tool enriches business activity monitoring with prediction capabilities. It is implemented by using complex event processing technology (CEP). The prediction component is trained with event log data of completed process instances. The knowledge obtained from this training, combined with event data of running process instances, allows for making predictions at intermediate execution stages on a currently running process instance's future behavior and on process metrics. preCEP comprises a learning component, a run-time environment as well as a modeling environment, and a visualization component of the predictions.

Keywords
Event-driven Process Analytics, Business Activity Monitoring, Complex Event Processing, Business Process Management, Operational Business Intelligence



Publication type
Conference Paper

Peer reviewed
Yes

Publication status
Published

Year
2013

Conference
International Conference on Design Science Research in Information Systems and Technology (DESRIST)

Venue
Helsinki, Finland

Book title
Design Science at the Intersection of Physical and Virtual Design: Proceedings of the 8th International Conference DESRIST 2013

Editor
Brocke Jan vom, Hekkala Riitta, Ram Sudha, Rossi Matti

Start page
448

End page
455

Volume
7939

Title of series
Lecture Notes in Computer Science

Publisher
Springer

Place
Heidelberg

Language
English

ISBN
978-3-642-3882

DOI

Full text