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