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.
Work funded by grants RTI2018-101204-B-C21 and RTI2018-101204-B-C22 funded by MCIN/ AEI/ 10.13039/501100011033/ and ERDF A way of making Europe; grant P18-FR-2895 and US-1381595 funded by Junta de Andalucía/ERDF, UE.
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Márquez-Chamorro, A.E., Nepomuceno-Chamorro, I.A., Resinas, M., Ruiz-Cortés, A. (2022). Updating Prediction Models for Predictive Process Monitoring. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds) Advanced Information Systems Engineering. CAiSE 2022. Lecture Notes in Computer Science, vol 13295. Springer, Cham. https://doi.org/10.1007/978-3-031-07472-1_18
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