Remaining cycle time prediction with Graph Neural Networks for Predictive Process Monitoring

Abstract

Predictive process monitoring is at the intersection of machine learning and process mining. This subfield of process mining leverages historical data generated from process executions to make predictions about the ongoing process. One of the predictive process monitoring tasks with high interest is predicting the remaining cycle time of process instances. Recently, deep neural networks, particularly long short-term memory, have attracted much attention due to their ability to learn relevant features automatically and predict with high accuracy. While these models require data defined in the Euclidean space, graph neural networks have the advantage of handling data that can be represented as graphs. This paper proposes the use of graph neural network models to predict the remaining cycle time, which has not yet been studied in the literature. The proposed models are evaluated on real-life event logs and compared to a state-of-the-art long short-term memory model. The results show that graph neural network models can improve prediction accuracy, particularly for complex processes.