Improving Predictive Process Monitoring Through Reachability Graph-Based Masking of Neural Networks

Abstract

Predicting the next event during process runtime is an objective of interest in predictive process monitoring (PPM). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Decay replay mining</i> is one of few deep learning-based next event prediction approaches that are built upon process model notations. However, this algorithm does not fully intertwine its neural network with the available process knowledge contained in the process model. This work, which is an extended version of an earlier conference publication, investigates the reachability graphs of underlying Petri net process models for masking the neural network of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decay replay mining</i> to ultimately increase the quality of next event predictions. A more comprehensive set of experiments is performed to provide robust statistical evidence of the usefulness of the approach and relativizes earlier made claims and hypotheses. In addition, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decay replay mining</i> approach is applied with the suggested reachability graph-based masking extension to a healthcare use case of sepsis patients facilitating decision-making for healthcare practitioners. The obtained results further underscore the validity of the masking of neural networks using knowledge contained in the reachability graph of a Petri net process model.