Adaptive trace abstraction approach for predicting business process performances

This paper presents an approach to the discovery of predictive process models, which combines a series of data mining techniques (ranging from pattern mining, to non-parametric regression and to predictive clustering) with ad-hoc data transformation and abstraction mechanisms. As a result, a modular representation of the process is obtained, where different performance-relevant variants of it are provided with separate regression models, and discriminated on the basis of context information. As the approach can look at the given log traces at a proper level of abstraction, in a pretty automatic and transparent fashion, no heavy intervention by the analyst is required (a major drawback of previous solutions in the literature). Tests performed on a real application scenario showed satisfactory results, in terms of both prediction accuracy and robustness.