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preCEP: Facilitating Predictive Event-driven Process Analytics


Bernd Schwegmann, Martin Matzner, Christian Janiesch



Published: 2013 Juni
Herausgeber: Jan vom Brocke et al.
Buchtitel: Proceedings of the 8th Design Science Research in Information Systems and Technologies (DESRIST) Products and Prototypes Track
Reihe: Lecture Notes in Computer Science 7939
Seiten: 448-455
Verlag: Springer
Erscheinungsort: Helsinki
Referierte Veröffentlichung
BibTeX

Kurzfassung
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.



Forschungsgruppe

Ökonomie und Technologie der eOrganisation


Forschungsgebiet
Business Activity Management, Business Intelligence, Complex Event Processing, Geschäftsprozessanalyse, Geschäftsprozessmanagement