Deep Learning Models for Predictive Monitoring of Business Processes

• Autores: Efrén Rama Maneiro
• Directores de la Tesis: Manuel Lama Penín (dir. tes.) , Juan Carlos Vidal Aguiar (dir. tes.)
• Lectura: En la Universidade de Santiago de Compostela ( España ) en 2023
• Idioma: inglés
• Tribunal Calificador de la Tesis: Manuel Resinas Arias de Reyna (presid.) , Alberto José Bugarín Diz (secret.) , Fabio Patrizi (voc.)
• Programa de doctorado: Programa de Doctorado en Investigación en Tecnologías de la Información por la Universidad de A Coruña y la Universidad de Santiago de Compostela
• Menciones:
  • Internacional
• Calificación: Sobresaliente Cum Laude
• Enlaces
  • Tesis en acceso abierto en: MINERVA
• Resumen

  • 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.