# Deep Learning for Predictive Business Process Monitoring: Review and Benchmark

arXiv: https://arxiv.org/abs/2009.13251 · HTML: https://mlsanity.com/paper/2009.13251/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 30 of 45)

- [ProcessTransformer: Predictive Business Process Monitoring with Transformer Network](https://mlsanity.com/paper/2104.00721/) (2021)
- [PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances](https://mlsanity.com/paper/2404.06267/) (2024)
- [Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring](https://mlsanity.com/paper/2112.09641/) (2021)
- [Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning](https://mlsanity.com/paper/2103.11944/) (2021)
- [SNAP: Semantic Stories for Next Activity Prediction](https://mlsanity.com/paper/2401.15621/) (2024)
- [Neuro-Symbolic Predictive Process Monitoring](https://mlsanity.com/paper/2509.00834/) (2025)
- [Evaluating Predictive Business Process Monitoring Approaches on Small Event Logs](https://mlsanity.com/paper/2104.00362/) (2021)
- [Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms](https://mlsanity.com/paper/2109.01419/) (2021)
- [Trace Encoding in Process Mining: a survey and benchmarking](https://mlsanity.com/paper/2301.02167/) (2023)
- [Domain Adaptation of LLMs for Process Data](https://mlsanity.com/paper/2509.03161/) (2025)
- [Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions](https://mlsanity.com/paper/2205.05446/) (2022)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Self-Explaining Neural Networks for Business Process Monitoring](https://mlsanity.com/paper/2503.18067/) (2025)
- [Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring](https://mlsanity.com/paper/2506.03696/) (2025)
- [Tailoring Machine Learning for Process Mining](https://mlsanity.com/paper/2306.10341/) (2023)
- [A Discussion on Generalization in Next-Activity Prediction](https://mlsanity.com/paper/2309.09618/) (2023)
- [Explainable Artificial Intelligence for Improved Modeling of Processes](https://mlsanity.com/paper/2212.00695/) (2022)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Recent Advances in Data-Driven Business Process Management](https://mlsanity.com/paper/2406.01786/) (2024)
- [An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoring](https://mlsanity.com/paper/2502.10573/) (2025)
- [Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.09527/) (2025)
- [ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods](https://mlsanity.com/paper/2509.00797/) (2025)
- [Nirdizati: an Advanced Predictive Process Monitoring Toolkit](https://mlsanity.com/paper/2210.09688/) (2022)
- [Enhancing the Accuracy of Predictors of Activity Sequences of Business Processes](https://mlsanity.com/paper/2312.05560/) (2023)
- [From Source to Target: Leveraging Transfer Learning for Predictive Process Monitoring in Organizations](https://mlsanity.com/paper/2508.08061/) (2025)
- [Time Series Foundation Models for Process Model Forecasting](https://mlsanity.com/paper/2512.07624/) (2025)
- [Evaluating SAP RPT-1 for Enterprise Business Process Prediction: In-Context Learning vs. Traditional Machine Learning on Structured SAP Data](https://mlsanity.com/paper/2602.19237/) (2026)
- [A Systematic Literature Review on Process-Aware Recommender Systems](https://mlsanity.com/paper/2103.16654/) (2021)
- [Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring](https://mlsanity.com/paper/2211.16106/) (2022)
- [Analysis of business process automation as linear time-invariant system network](https://mlsanity.com/paper/2302.04785/) (2023)

## Abstract (arXiv)

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.