mlsanityData to 7 Oct 2026

Deep Learning for Predictive Business Process Monitoring: Review and Benchmark

arXiv 2009.13251 ↗First posted 24 Sep 2020cs.LG cs.AIcited by 45

Not on the map. Why?

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.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 4; 2023: 8; 2024: 5; 2025: 16; 2026: 6.

45 papers.

Sort:

  1. 2021ProcessTransformer: Predictive Business Process Monitoring with Transformer Networkcited by 34
  2. 2024PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instancescited by 13
  3. 2021Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoringcited by 12
  4. 2021Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learningcited by 10
  5. 2024SNAP: Semantic Stories for Next Activity Predictioncited by 8
  6. 2025Neuro-Symbolic Predictive Process Monitoringcited by 7
  7. 2021Evaluating Predictive Business Process Monitoring Approaches on Small Event Logscited by 6
  8. 2021Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanismscited by 6
  9. 2023Trace Encoding in Process Mining: a survey and benchmarkingcited by 6
  10. 2025Domain Adaptation of LLMs for Process Datacited by 6
  11. 2024Machine learning in business process management: A systematic literature reviewcited by 5
  12. 2025Self-Explaining Neural Networks for Business Process Monitoringcited by 5
33 more
  1. 2022Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directionscited by 4
  2. 2023Tailoring Machine Learning for Process Miningcited by 4
  3. 2023A Discussion on Generalization in Next-Activity Predictioncited by 4
  4. 2025Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoringcited by 4
  5. 2022Explainable Artificial Intelligence for Improved Modeling of Processescited by 2
  6. 2023A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areascited by 2
  7. 2024Recent Advances in Data-Driven Business Process Managementcited by 2
  8. 2025An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoringcited by 2
  9. 2025Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoringcited by 2
  10. 2025ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methodscited by 2
  11. 2022Nirdizati: an Advanced Predictive Process Monitoring Toolkitcited by 1
  12. 2023Enhancing the Accuracy of Predictors of Activity Sequences of Business Processescited by 1
  13. 2025From Source to Target: Leveraging Transfer Learning for Predictive Process Monitoring in Organizationscited by 1
  14. 2025Time Series Foundation Models for Process Model Forecastingcited by 1
  15. 2021A Systematic Literature Review on Process-Aware Recommender Systemscited by 0
  16. 2022Encoder-Decoder Model for Suffix Prediction in Predictive Monitoringcited by 0
  17. 2023Analysis of business process automation as linear time-invariant system networkcited by 0
  18. 2023CoSMo: a Framework to Instantiate Conditioned Process Simulation Modelscited by 0
  19. 2023Inter-case Predictive Process Monitoring: A candidate for Quantum Machine Learning?cited by 0
  20. 2024Extending predictive process monitoring for collaborative processescited by 0
  21. 2025Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatmentcited by 0
  22. 2025Working My Way Back to You: Resource-Centric Next-Activity Predictioncited by 0
  23. 2025How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestampscited by 0
  24. 2025On the Simplification of Neural Network Architectures for Predictive Process Monitoringcited by 0
  25. 2025Actor-Enriched Time Series Forecasting of Process Performancecited by 0
  26. 2025Auto-ML Graph Neural Network Hypermodels for Outcome Prediction in Event-Sequence Datacited by 0
  27. 2025Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Librarycited by 0
  28. 2026Evaluating SAP RPT-1 for Enterprise Business Process Prediction: In-Context Learning vs. Traditional Machine Learning on Structured SAP Datacited by 0
  29. 2026David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLMcited by 0
  30. 2026Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approachescited by 0
  31. 2026D-TAIA: Domain-Aware LLM Adaptation for Multi-Task Predictive Process Monitoringcited by 0
  32. 2026GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networkscited by 0
  33. 2026Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processescited by 0