Deep Learning Models for Predictive Monitoring of Business Processes
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http://hdl.handle.net/10347/32387
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Título: | Deep Learning Models for Predictive Monitoring of Business Processes |
Autor/a: | Rama Maneiro, Efrén |
Dirección/Titoría: | Lama Penín, Manuel Vidal Aguiar, Juan Carlos |
Centro/Departamento: | Universidade de Santiago de Compostela. Escola de Doutoramento Internacional (EDIUS) Universidade de Santiago de Compostela. Programa de Doutoramento en Investigación en Tecnoloxías da Información |
Palabras chave: | Process mining | predictive monitoring | deep learning | graph neural networks | reinforcement learning | |
Data: | 2023 |
Resumo: | 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. |
Data de Embargo: | 2024-12-18 |
URI: | http://hdl.handle.net/10347/32387 |
Dereitos: | Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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