Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring

Abstract

Predictive monitoring of business processes is a subfield of process mining that aims to predict, among other things, the characteristics of the next event or the sequence of the next events. Although multiple approaches based on deep learning have been proposed, mainly recurrent neural networks and convolutional neural networks, none of them really exploit the structural information available in process models. This paper proposes an approach that simultaneously learns spatio-temporal information from both the event log and the processmodel by combining recurrent neural networks with graph convolutional networks. Thus, common patterns from process models, such as loops or parallels, can be learned while avoiding overwriting information during the encoding phase. An experimental evaluation of real-life event logs shows that our approach is more consistent and outperforms the current state-of-the-art approaches.