  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, most of the
state-of-the-art methods for predictive monitoring require the training of
complex machine learning models, which is often inefficient. Moreover, most of
these methods require a hyper-parameter optimization that requires several
repetitions of the training process which is not feasible in many real-life
applications. In this paper, we propose an instance selection procedure that
allows sampling training process instances for prediction models. We show that
our instance selection procedure allows for a significant increase of training
speed for next activity and remaining time prediction methods while maintaining
reliable levels of prediction accuracy.
