A Deep Learning Approach for Predicting Process Behaviour at Runtime

Joerg Evermann; Jana-Rebecca Rehse; Peter Fettke

In: Marlon Dumas; Marcelo Fantinato (Hrsg.). Proceedings of the 1st International Workshop on Runtime Analysis of Process-Aware Information Systems. International Workshop on Runtime Analysis of Process-Aware Information Systems (PRAISE-2016), located at International Conference on Business Process Management, September 18-22, Rio de Janeiro, Brazil, Springer, 2016.


Predicting the final state of a running process, the remaining time to completion or the next activity of a running process are important aspects of runtime process management. Runtime management requires the ability to identify processes that are at risk of not meeting certain criteria in order to offer case managers decision information for timely intervention. This in turn requires accurate prediction models for process outcomes and for the next process event, based on runtime information available at the prediction and decision point. In this paper, we describe an initial application of deep learning with recurrent neural networks to the problem of predicting the next process event. This is both a novel method in process prediction, which has previously relied on explicit process models in the form of Hidden Markov Models (HMM) or annotated transition systems, and also a novel application for deep learning methods.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence