A Prediction Framework for Proactively Monitoring Aggregate Process-Performance Indicators

Abstract

Monitoring the performances of a business process is a key issue in many organizations, especially when predefined constraints exist on them, due to contracts or internal requirements. Several approaches were defined recently in the literature for predicting the performances of a single process instance. However, in many real situations, process-oriented performance metrics and associated constraints are defined in an aggregated form, on a time-window basis. This work right addresses the problem of predicting whether (the process instances in) each time window will infringe an aggregate performance constraint, at a series of checkpoints within the window. To this end, at each checkpoint, three kinds of measures are to be estimated: what performance outcome each ongoing process instance will yield, how many process instances will start in the rest of the window, and what their aggregate performance outcomes will be. The approach proposed is general (it can reuse a wide range of regression methods), and it can be embedded in a continuous monitoring-and-learning scheme. Tests on real-life logs showed its validity in terms of prediction accuracy.