  Predictive process monitoring is a subfield of process mining that aims to
estimate case or event features for running process instances. Such predictions
are of significant interest 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. This paper
proposes an instance selection procedure that allows sampling training process
instances for prediction models. We show that our sampling method allows for a
significant increase of training speed for next activity prediction methods
while maintaining reliable levels of prediction accuracy.
