A Data-Driven Prediction Framework for Analyzing and Monitoring Business Process Performances

This paper presents a framework for analyzing and predicting the performances of a business process, based on historical data gathered during its past enactments. The framework hinges on an inductive-learning technique for discovering a special kind of pre- dictive process models, which can support the run-time prediction of a given performance measure (e.g., the remaining processing time/steps) for an ongoing process instance, based on a modular representation of the process, where major performance-relevant variants of it are mod- eled with different regression models, and discriminated on the basis of context variables. The technique is an original combination of different data mining methods (ranging from pattern mining, to non-parametric regression and predictive clustering) and ad-hoc data transformation mechanisms, allowing for looking at the log traces at a proper level of abstraction, in a pretty automatic and transparent way. The technique has been integrated in a performance monitoring architecture, meant to provide managers and analysts (and possibly the process enactment envi- ronment) with continuously updated performance statistics, as well as with the anticipated notification of likely SLA violations. The approach has been validated on a real-life case study, with satisfactory results, in terms of both prediction accuracy and robustness.