Forecasting Business Process Remaining Time Through Deep Learning Approaches - Oliveira da Silva, Ronildo; Pires Magalhães, Regis; Almada Cruz, Lívia; Pereira de Souza, Criston; Romero de Vasconcelos, Davi; Antônio Fernandes de Macedo, José (Journal of Information and Data Management, 2025)
Business process analysis is a part of process mining, which involves predictive monitoring. It seeks to predict individual processes, such as determining the next step to execute based on past events or estimating the remaining time until process completion. Such predictions can help to prevent waits, discover process bottlenecks, and assist alert systems. This paper aims to evaluate deep learning architectures to predict the time required to complete a business process instance. We have evaluated the models using three real datasets, including two widely used public ones. The experimental results show deep learning architectures that combined dense layers with a self-attention mechanism outperformed the current state-of-the-art, demonstrating superior performance regarding the mean absolute error metric in most of the datasets analyzed.