Andrea Burattin, Ph.D.

Assistant Professor

Data-Aware Remaining Time Prediction of Business Process Instances

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.

Paper Information and Files

Published in Proceedings of the International Joint Conference on Neural Networks (IEEE WCCI IJCNN 2014); Beijing, China; July 6-11, 2014.

DOI: 10.1109/IJCNN.2014.6889360