mlsanityData to 7 Oct 2026

Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring

arXiv 1805.02896 ↗First posted 8 May 2018cs.AI cs.LGcited by 43

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Abstract (arXiv)

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g. shifting resources from one case onto another to ensure this latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 16 real-life datasets originating from different industry domains.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2018.

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Citing papers per year: 2021: 10; 2022: 10; 2023: 7; 2024: 5; 2025: 10; 2026: 1.

43 papers.

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  1. 2021A systematic literature review on state-of-the-art deep learning methods for process predictioncited by 32
  2. 2021Prescriptive Process Monitoring for Cost-Aware Cycle Time Reductioncited by 17
  3. 2021Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoringcited by 16
  4. 2021DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approachcited by 13
  5. 2024PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instancescited by 13
  6. 2022Counterfactual Explanations for Predictive Business Process Monitoringcited by 12
  7. 2022When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraintscited by 11
  8. 2023An Assessment of ChatGPT on Log Datacited by 11
  9. 2023Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspectivecited by 10
  10. 2021Evaluating Predictive Business Process Monitoring Approaches on Small Event Logscited by 6
  11. 2021Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanismscited by 6
  12. 2022Object-centric Process Predictive Analyticscited by 6
31 more
  1. 2024Machine learning in business process management: A systematic literature reviewcited by 5
  2. 2024Achieving Group Fairness through Independence in Predictive Process Monitoringcited by 5
  3. 2021Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processescited by 4
  4. 2022Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directionscited by 4
  5. 2023Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoringcited by 4
  6. 2025Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoringcited by 4
  7. 2022Explainable Predictive Process Monitoring: A User Evaluationcited by 3
  8. 2022Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoringcited by 3
  9. 2022Predictive Object-Centric Process Monitoringcited by 3
  10. 2023Performance-Preserving Event Log Sampling for Predictive Monitoringcited by 3
  11. 2021Explainable AI Enabled Inspection of Business Process Prediction Modelscited by 2
  12. 2023A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areascited by 2
  13. 2024Recent Advances in Data-Driven Business Process Managementcited by 2
  14. 2025Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoringcited by 2
  15. 2025ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methodscited by 2
  16. 2021Process Model Forecasting Using Time Series Analysis of Event Sequence Datacited by 1
  17. 2022XAI in the context of Predictive Process Monitoring: Too much to Revealcited by 1
  18. 2022Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighborscited by 1
  19. 2022Nirdizati: an Advanced Predictive Process Monitoring Toolkitcited by 1
  20. 2023Enhancing the Accuracy of Predictors of Activity Sequences of Business Processescited by 1
  21. 2025Log Optimization Simplification Method for Predicting Remaining Timecited by 1
  22. 2025From Source to Target: Leveraging Transfer Learning for Predictive Process Monitoring in Organizationscited by 1
  23. 2025A Human-In-The-Loop Approach for Improving Fairness in Predictive Business Process Monitoringcited by 1
  24. 2021A Systematic Literature Review on Process-Aware Recommender Systemscited by 0
  25. 2023Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation stylescited by 0
  26. 2024Unraveling the Never-Ending Story of Lifecycles and Vitalizing Processescited by 0
  27. 2025An Uncertainty-Aware ED-LSTM for Probabilistic Suffix Predictioncited by 0
  28. 2025The Role of Explanation Styles and Perceived Accuracy on Decision Making in Predictive Process Monitoringcited by 0
  29. 2025How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestampscited by 0
  30. 2025SCOPE: Sequential Causal Optimization of Process Interventionscited by 0
  31. 2026Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logscited by 0