Abstract
Deep learning models are now being increasingly used for predictive process mining tasks in business processes. Modern approaches have been successful in achieving better performance for different predictive tasks, as compared to traditional approaches. In this work, five different variants of a model involving a Graph Convolutional Layer and linear layers have been tested for the task of predicting the nature and timestamp of the next activity in a given process instance. We have introduced a new method for representing feature vectors for any individual event in a given process instance, taking into consideration the structure of Directly-follows process graphs generated from the corresponding datasets. The adjacency matrix of the process graphs generated has been used as input to a Graph Convolutional Network (GCN). Different model variants make use of variations in the representation of the adjacency matrix. The performance of all the model variants have been tested at different stages of a process, determined by quartiles estimated based on the number of events and the case duration. The results obtained from the experiments, significantly improves over the previously reported results for most of the individual tasks. Interestingly, it was observed that a linear Multi-Layer Perceptron (MLP) with dropout was able to outperform the GCN variants in both the prediction tasks. Using a quartile-based analysis, it was further observed that the other variants were able to perform better than MLP at individual quartiles in some of the tasks where the MLP had the best overall performance.
Abstract (translated)
URL
https://arxiv.org/abs/2102.07838
