Deep Learning Models for Predictive Monitoring of Business Processes

Abstract

In this thesis, we enhance predictive monitoring in process mining through the use of advanced deep-learning techniques. By integrating Graph Neural Networks with Recurrent Neural Networks, we learn directly from the process model while also considering event sequences. We introduce two neural models: the first aims to predict the next activity in a business process, while the second forecasts the remaining sequence of activities until the case finishes. For the latter problem, a new Reinforcement Learning model is also proposed to dynamically learn optimal activity selection strategies during training. All models are rigorously validated using real-world event logs under a novel evaluation methodology to facilitate robust and fair comparisons between different predictive monitoring approaches.