Multi-perspective enriched instance graphs for next activity prediction through graph neural network

Today’s organizations store lots of data tracking the execution of their business processes. These data often contain valuable information that can be used to predict the evolution of running process executions. The present paper investigates the combined use of Instance Graphs and Deep Graph Convolutional Neural Networks to predict which activity will be performed next given a partial process execution. In addition to the exploitation of graph structures to encode the control-flow information, we investigate how to couple it with additional data perspectives. Experiments show the feasibility of the proposed approach, whose outcomes are consistently placed in the top ranking then compared to those obtained by well-known state-of-the-art approaches.