Data-Aware Remaining Time Prediction of Business Process Instances

M. Polato, A. Sperduti, A. Burattin and M. de Leoni

In: International Joint Conference on Neural Networks (IEEE WCCI IJCNN) (accepted), 2014.

Abstract. Accurate prediction of the completion time of a business process instance would constitute a valuable tool when managing processes under service level agreement constraints. Such prediction, however, is a very challenging task. A wide variety of factors could influence the trend of a process instance, and hence just using time statistics of historical cases cannot be sufficient to get accurate predictions. Here we propose a new approach where, in order to improve the prediction quality, both the control and the data flow perspectives are jointly used. To achieve this goal, our approach builds a process model which is augmented by time and data information in order to enable remaining time prediction. The remaining time prediction of a running case is calculated combining two factors: (a) the likelihood of all the following activities, given the data collected so far; and (b) the remaining time estimation given by a regression model built upon the data.


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Andrea Burattin