Deep Learning for Predictive Business Process Monitoring: Review and Benchmark

TL;DR

This paper reviews deep learning methods for predictive business process monitoring and provides a comprehensive benchmark evaluation of 10 approaches across 12 datasets, highlighting their strengths and weaknesses.

Contribution

It offers the first systematic review combined with an extensive experimental benchmark of deep learning approaches for predictive process monitoring.

Findings

Deep learning approaches vary significantly in performance.

Benchmark results identify the most effective methods for specific logs.

The study highlights the need for standardized evaluation protocols.

Abstract

Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive monitoring based on these techniques. However, the high disparity of process logs and experimental setups used to evaluate these approaches makes it especially difficult to make a fair comparison. Furthermore, it also difficults the selection of the most suitable approach to solve a specific problem. In this paper, we provide both a systematic literature review of approaches that use deep learning to tackle the predictive monitoring tasks. In addition, we performed an exhaustive experimental evaluation of 10 different approaches over 12 publicly available process logs.