Explainable predictive business process monitoring using gated graph neural networks

Abstract

ABSTRACT Predictive business process monitoring (PBPM) is a class of techniques designed to forecast future behaviour of a running process instance or the value of process-related metrics like times and frequencies. PBPM systems support process workers and process managers in making operational decisions. State-of-the-art PBPM systems apply deep-learning techniques with multiple hidden layers to infer from data which makes it difficult for system users to understand why a prediction was made. However, the user needs to see deeper causes to identify intervention mechanisms that secure process performance. The main contribution of this paper is a technique that makes a prediction more explainable by visualising how much the different activities included in a process impacted the prediction. This work is the first to use gated graph neural networks (GGNNs) to make decisions more explainable, and it’s also GGNN’s first application to PBPM. We use a process event data set to demonstrate our approach.