A Predictive Learning Framework for Monitoring Aggregated Performance Indicators over Business Process Events

In many application contexts, a business process' executions are subject to performance constraints expressed in an aggregated form, usually over predefined time windows, and detecting a likely violation to such a constraint in advance could help undertake corrective measures for preventing it. This paper illustrates a prediction-aware event processing framework that addresses the problem of estimating whether the process instances of a given (unfinished) windoww will violate an aggregate performance constraint, based on the continuous learning and application of an ensemble of models, capable each of making and integrating two kinds of predictions: singleinstance predictions concerning the ongoing process instances of w, and time-series predictions concerning the "future" process instances ofw (i.e. those that have not started yet, but will start by the end of w). Notably, the framework can continuously update the ensemble, fully exploiting the raw event data produced by the process under monitoring, suitably lifted to an adequate level of abstraction. The framework has been validated against historical event data coming from real-life business processes, showing promising results in terms of both accuracy and efficiency.