Performance-Preserving Event Log Sampling for Predictive Monitoring

Abstract

Predictive monitoring is the subfield of process mining that aims to obtain estimations of cases or events features for running process instances. Such predictions are of significant value to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. In this paper, we propose an instance selection method that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for predictive monitoring methods while maintaining reliable levels of prediction accuracy.

Publication
International Workshop on Leveraging Machine Learning in Process Mining (ML4PM), Accepted