Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks

TL;DR

This paper introduces a novel self-supervised learning approach for representing business process instances using Gramian Angular Fields and CNNs, enabling effective process prediction, clustering, and anomaly detection.

Contribution

It proposes a new method combining Gramian Angular Fields with CNNs for comprehensive business process representation learning, including a self-supervised pre-training strategy.

Findings

Effective visualization of process representations.

Improved accuracy in process prediction tasks.

Enhanced case retrieval capabilities.

Abstract

Learning meaningful representations of data is an important aspect of machine learning and has recently been successfully applied to many domains like language understanding or computer vision. Instead of training a model for one specific task, representation learning is about training a model to capture all useful information in the underlying data and make it accessible for a predictor. For predictive process analytics, it is essential to have all explanatory characteristics of a process instance available when making predictions about the future, as well as for clustering and anomaly detection. Due to the large variety of perspectives and types within business process data, generating a good representation is a challenging task. In this paper, we propose a novel approach for representation learning of business process instances which can process and combine most perspectives in an event log. In conjunction with a self-supervised pre-training method, we show the capabilities of the approach through a visualization of the representation space and case retrieval. Furthermore, the pre-trained model is fine-tuned to multiple process prediction tasks and demonstrates its effectiveness in comparison with existing approaches.