Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring

• URL: http://arxiv.org/abs/2112.09641v1
• Date: Fri, 17 Dec 2021 17:30:30 GMT
• Title: Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring
• Authors: Efr\'en Rama-Maneiro, Juan C. Vidal, Manuel Lama
• Abstract summary: This paper proposes an approach based on graph convolutional networks and recurrent neural networks. An experimental evaluation on real-life event logs shows that our approach is more consistent and outperforms the current state-of-the-art approaches.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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 based on graph convolutional networks and recurrent neural networks that uses information directly from the process model. An experimental evaluation on real-life event logs shows that our approach is more consistent and outperforms the current state-of-the-art approaches.

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