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Predictive monitoring of business processes is a subfield of process miningthat aims to predict, among other things, the characteristics of the next eventor the sequence of next events. Although multiple approaches based on deeplearning have been proposed, mainly recurrent neural networks and convolutionalneural networks, none of them really exploit the structural informationavailable in process models. This paper proposes an approach based on graphconvolutional networks and recurrent neural networks that uses informationdirectly from the process model. An experimental evaluation on real-life eventlogs shows that our approach is more consistent and outperforms the currentstate-of-the-art approaches.

Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring