Knowledge-Driven Modulation of Neural Networks with Attention Mechanism
  for Next Activity Prediction
Ivan Donadello Jonghyeon Ko Fabrizio Maria Maggi Jan Mendling Francesco Riva Matthias Weidlich
Predictive Process Monitoring (PPM) aims at leveraging historic process
execution data to predict how ongoing executions will continue up to their
completion. In recent years, PPM techniques for the prediction of the next
activities have matured significantly, mainly thanks to the use of Neural
Networks (NNs) as a predictor. While their performance is difficult to beat in
the general case, there are specific situations where background process
knowledge can be helpful. Such knowledge can be leveraged for improving the
quality of predictions for exceptional process executions or when the process
changes due to a concept drift. In this paper, we present a Symbolic[Neuro]
system that leverages background knowledge expressed in terms of a procedural
process model to offset the under-sampling in the training data. More
specifically, we make predictions using NNs with attention mechanism, an
emerging technology in the NN field. The system has been tested on several
real-life logs showing an improvement in the performance of the prediction
task.