Deep Learning for Predictive Business Process Monitoring: Review and Benchmark
This work provides a standardized evaluation framework for researchers and practitioners in business process management, though it is incremental as it consolidates existing methods rather than introducing new ones.
The paper conducted a systematic literature review and benchmarked 10 deep learning approaches for predictive business process monitoring across 12 public process logs to address the difficulty in fair comparison and selection due to high disparity in data and setups.
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.