ABSTRACT
Predictive monitoring is a key activity in some Process-Aware Information Systems (PAIS) such as information systems for operational management support. Unforeseen circumstances like COVID can introduce changes in human behaviour, processes, or computing resources, which lead the owner of the process or information system to consider whether the quality of the predictions made by the system (e.g., mean time to solution) is still good enough, and if not, which amount of data and how often the system should be trained to maintain the quality of the predictions. To answer these questions, we propose, compare, and evaluate different strategies for selecting the amount of information required to update the predictive model in a context of offline learning. We performed an empirical evaluation using three real-world datasets that span between 2 and 13 years to validate the different strategies which show a significant enhancement in the prediction accuracy with respect to a non-update strategy. © 2022, Springer Nature Switzerland AG.