Classifying Process Instances Using Recurrent Neural Networks

TL;DR

This paper explores the use of recurrent neural networks, especially GRU, for classifying process instances in process mining, demonstrating faster training times with comparable accuracy to LSTM.

Contribution

It is the first to apply GRU to classifying business process instances and introduces activity filtering to improve training efficiency.

Findings

GRU outperforms LSTM in training time.

Filtering infrequent activities improves training efficiency.

GRU achieves similar accuracy to LSTM.

Abstract

Process Mining consists of techniques where logs created by operative systems are transformed into process models. In process mining tools it is often desired to be able to classify ongoing process instances, e.g., to predict how long the process will still require to complete, or to classify process instances to different classes based only on the activities that have occurred in the process instance thus far. Recurrent neural networks and its subclasses, such as Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), have been demonstrated to be able to learn relevant temporal features for subsequent classification tasks. In this paper we apply recurrent neural networks to classifying process instances. The proposed model is trained in a supervised fashion using labeled process instances extracted from event log traces. This is the first time we know of GRU having been used in classifying business process instances. Our main experimental results shows that GRU outperforms LSTM remarkably in training time while giving almost identical accuracies to LSTM models. Additional contributions of our paper are improving the classification model training time by filtering infrequent activities, which is a technique commonly used, e.g., in Natural Language Processing (NLP).