  Predictive business process monitoring is concerned with the prediction how a
running process instance will unfold up to its completion at runtime. Most of
the proposed approaches rely on a wide number of different machine learning
(ML) techniques. In the last years numerous comparative studies, reviews, and
benchmarks of such approaches where published and revealed that they can be
successfully applied for different prediction targets. ML techniques require a
qualitatively and quantitatively sufficient data set. However, there are many
situations in business process management (BPM) where only a quantitatively
insufficient data set is available. The problem of insufficient data in the
context of BPM is still neglected. Hence, none of the comparative studies or
benchmarks investigates the performance of predictive business process
monitoring techniques in environments with small data sets. In this paper an
evaluation framework for comparing existing approaches with regard to their
suitability for small data sets is developed and exemplarily applied to
state-of-the-art approaches in predictive business process monitoring.
