As organizations gain awareness of the potential business value locked in their process execution event logs, “evidence-based” business process management (BPM) becomes a common tool for process analysts. In contrast to traditional process monitoring techniques which are typically performed using data from running process instances only, predictive evidence-based BPM methods tap also into historical data, to allow process workers to respond, in real-time, to specific process performance issues and compliance violations as they arise or even before they arise.
Nirdizati offers process workers an intuitive yet powerful dashboard that keeps track of ongoing business processes and displays predictive analytics in real-time. Nirdizati consists of two core components, namely Training and Runtime, integrated as two plugins into Apromore process analytics platform. The Training plugin takes as input a business process event log stored in the Apromore repository, and produces one or more predictive models, which can then be deployed to the runtime predictive monitoring environment. Once a model is deployed, the Runtime plugin bundle listens to a stream of events coming from an information system supporting the process, or produced by replaying an event log stored in the repository, and creates a stream of predictions. These predictions can then be visualized in a Web dashboard or exported into a text file to be used within third-party business intelligence tools.