Masking Neural Networks Using Reachability Graphs to Predict Process Events

TL;DR

This paper introduces a novel approach that integrates process models more tightly with neural networks for next event prediction by using reachability graphs and masking layers, improving predictive accuracy.

Contribution

It proposes a new method that interlocks process models with neural networks through reachability graph-based masking, enhancing prediction performance.

Findings

Improved next event prediction accuracy

Effective use of reachability graphs for masking

Enhanced neural network architecture for process prediction

Abstract

Decay Replay Mining is a deep learning method that utilizes process model notations to predict the next event. However, this method does not intertwine the neural network with the structure of the process model to its full extent. This paper proposes an approach to further interlock the process model of Decay Replay Mining with its neural network for next event prediction. The approach uses a masking layer which is initialized based on the reachability graph of the process model. Additionally, modifications to the neural network architecture are proposed to increase the predictive performance. Experimental results demonstrate the value of the approach and underscore the importance of discovering precise and generalized process models.