Business value can be lost if a decision maker’s action distance to the observation of a business event is too high. So far, two classes of information systems, which promise to assist decision makers, have been discussed independently from each other only: business intelligence systems that query historic business event data in order to prepare predictions of future process behavior and real-time monitoring systems. This paper suggests using real-time data for predictions following an event-driven approach. A predictive event-driven process analytics (edPA) method is presented which integrates aspects from business activity monitoring and process intelligence. Needs for procedure integration, metric quality, and the inclusion of actionable improvements are outlined. The method is implemented in the form of a software prototype and evaluated.
Schwegmann, Bernd; Matzner, Martin; and Janiesch, Christian, "A Method and Tool for Predictive Event-Driven Process Analytics" (2013). Wirtschaftsinformatik Proceedings 2013. 46.