  Researchers have proposed a variety of predictive business process monitoring
(PBPM) techniques aiming to predict future process behaviour during the process
execution. Especially, techniques for the next activity prediction anticipate
great potential in improving operational business processes. To gain more
accurate predictions, a plethora of these techniques rely on deep neural
networks (DNNs) and consider information about the context, in which the
process is running. However, an in-depth comparison of such techniques is
missing in the PBPM literature, which prevents researchers and practitioners
from selecting the best solution for a given event log. To remedy this problem,
we empirically evaluate the predictive quality of three promising DNN
architectures, combined with five proven encoding techniques and based on five
context-enriched real-life event logs. We provide four findings that can
support researchers and practitioners in designing novel PBPM techniques for
predicting the next activities.
