LAUSR

In this paper, we deal with one of the current challenges in process mining enhancement: the prediction of remaining times in business processes. Accurate predictions of the remaining time, defined as the required time for an instance process to finish, are critical in many systems for organizations being able to establish a priori requirements, for optimal management of resources or for improving the quality of the services organizations provide. Our approach consists of i) extracting and assessing a number of features on the business logs, that provide a structural characterization of the traces; ii) extending the well-known annotated transition system (ATS) model to include these features; iii) proposing a partitioning strategy for the lists of features associated to each state in the extended ATS; and iv) applying a linear regression technique to each partition for predicting the remaining time of new traces. Extensive experimentation using eight attributes and ten real-life datasets show that the proposed approach outperforms in terms of mean absolute error and accuracy all the other approaches in state of the art, which includes ATS-based, non-ATS based as well as Deep Learning-based approaches.