Predictive monitoring of temporally-aggregated performance indicators of business processes against low-level streaming events

Monitoring the performances of a business process is a key issue in many organizations, especially when the process must comply with predefined performance constraints. In such a case, empowering the monitoring system with prediction capabilities would allow us to know in advance a constraint violation, and possibly trigger corrective measures to eventually prevent the violation. Despite the problem of making run-time predictions for a process, based on pre-mortem log data, is an active research topic in Process Mining, current predictive monitoring approaches in this field only support predictions at the level of a single process instance, whereas process performance constraints are often defined in an aggregated form, according to predefined time windows. Moreover, most of these approaches cannot work well on the traces of a lowly-structured business process when these traces do not refer to well-defined process tasks/activities. For such a challenging setting, we define an approach to the problem of predicting whether the process instances of a given (unfinished) time window will violate an aggregate performance requirement. The approach mainly rely on inducing and integrating two complementary predictive models: (1) a clustering-based predictor for estimating the outcome of each ongoing process instance, (2) a time-series predictor for estimating the performance outcome of "future" process instances that will fall in the window after the moment when the prediction is being made (i.e. instances, not started yet, that will start by the end of the window). Both models are expected to benefit from the availability of aggregate context data regarding the environment that surrounds the process. This discovery approach is conceived as the core of an advanced performance monitoring system, for which an event-based conceptual architecture is here proposed. Tests on real-life event data confirmed the validity of our approach, in terms of accuracy, robustness, scalability, and usability. (C) 2018 Elsevier Ltd. All rights reserved.