In complex processes, various events can happen in different sequences. The
prediction of the next event activity given an a-priori process state is of
importance in such processes. Recent methods leverage deep learning techniques
such as recurrent neural networks to predict event activities from raw process
logs. However, deep learning techniques cannot efficiently model logical
behaviors of complex processes. In this paper, we take advantage of Petri nets
as a powerful tool in modeling logical behaviors of complex processes. We
propose an approach which first discovers Petri nets from event logs utilizing
a recent process mining algorithm. In a second step, we enhance the obtained
model with time decay functions to create timed process state samples. Finally,
we use these samples in combination with token movement counters and Petri net
markings to train a deep learning model that predicts the next event activity.
We demonstrate significant performance improvements and outperform the
state-of-the-art methods on eight out of nine real-world benchmark event logs
in accuracy.

Source link