  Predictive process monitoring is an evolving research field that studies how
to train and use predictive models for operational decision-making. One of the
problems studied in this field is that of predicting the sequence of upcoming
activities in a case up to its completion, a.k.a. the case suffix. The
prediction of case suffixes provides input to estimate short-term workloads and
execution times under different resource schedules. Existing methods to address
this problem often generate suffixes wherein some activities are repeated many
times, whereas this pattern is not observed in the data. Closer examination
shows that this shortcoming stems from the approach used to sample the
successive activity instances to generate a case suffix. Accordingly, the paper
introduces a sampling approach aimed at reducing repetitions of activities in
the predicted case suffixes. The approach, namely Daemon action, strikes a
balance between exploration and exploitation when generating the successive
activity instances. We enhance a deep learning approach for case suffix
predictions using this sampling approach, and experimentally show that the
enhanced approach outperforms the unenhanced ones with respect to control-flow
accuracy measures.
