  Process mining enables the reconstruction and evaluation of business
processes based on digital traces in IT systems. An increasingly important
technique in this context is process prediction. Given a sequence of events of
an ongoing trace, process prediction allows forecasting upcoming events or
performance measurements. In recent years, multiple process prediction
approaches have been proposed, applying different data processing schemes and
prediction algorithms. This study focuses on deep learning algorithms since
they seem to outperform their machine learning alternatives consistently.
Whilst having a common learning algorithm, they use different data
preprocessing techniques, implement a variety of network topologies and focus
on various goals such as outcome prediction, time prediction or control-flow
prediction. Additionally, the set of log-data, evaluation metrics and baselines
used by the authors diverge, making the results hard to compare. This paper
attempts to synthesise the advantages and disadvantages of the procedural
decisions in these approaches by conducting a systematic literature review.
