mlsanityData to 7 Oct 2026

Outcome-Oriented Predictive Process Monitoring: Review and Benchmark

arXiv 1707.06766 ↗First posted 21 Jul 2017cs.AIcited by 37

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Abstract (arXiv)

Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces. Motivated by the increasingly pervasive availability of fine-grained event data about business process executions, the problem of predictive process monitoring has received substantial attention in the past years. In particular, a considerable number of methods have been put forward to address the problem of outcome-oriented predictive process monitoring, which refers to classifying each ongoing case of a process according to a given set of possible categorical outcomes - e.g., Will the customer complain or not? Will an order be delivered, canceled or withdrawn? Unfortunately, different authors have used different datasets, experimental settings, evaluation measures and baselines to assess their proposals, resulting in poor comparability and an unclear picture of the relative merits and applicability of different methods. To address this gap, this article presents a systematic review and taxonomy of outcome-oriented predictive process monitoring methods, and a comparative experimental evaluation of eleven representative methods using a benchmark covering 24 predictive process monitoring tasks based on nine real-life event logs.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jul 2017.

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Citing papers per year: 2021: 10; 2022: 10; 2023: 9; 2024: 5; 2025: 3.

37 papers.

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  1. 2021A systematic literature review on state-of-the-art deep learning methods for process predictioncited by 32
  2. 2021Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoringcited by 16
  3. 2021Process Outcome Prediction: CNN vs. LSTM (with Attention)cited by 12
  4. 2021Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approachcited by 12
  5. 2022Counterfactual Explanations for Predictive Business Process Monitoringcited by 12
  6. 2022When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraintscited by 11
  7. 2023Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspectivecited by 10
  8. 2021How do I update my model? On the resilience of Predictive Process Monitoring models to changecited by 7
  9. 2022Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patternscited by 7
  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. 2023Trace Encoding in Process Mining: a survey and benchmarkingcited by 6
25 more
  1. 2022Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Modelscited by 5
  2. 2024Machine learning in business process management: A systematic literature reviewcited by 5
  3. 2022Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directionscited by 4
  4. 2023CREATED: Generating Viable Counterfactual Sequences for Predictive Process Analyticscited by 4
  5. 2023Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoringcited by 4
  6. 2023Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approachcited by 4
  7. 2022Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?cited by 3
  8. 2022The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detectioncited by 3
  9. 2023Performance-Preserving Event Log Sampling for Predictive Monitoringcited by 3
  10. 2024Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoringcited by 3
  11. 2023A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areascited by 2
  12. 2021Process Model Forecasting Using Time Series Analysis of Event Sequence Datacited by 1
  13. 2022XAI in the context of Predictive Process Monitoring: Too much to Revealcited by 1
  14. 2022Nirdizati: an Advanced Predictive Process Monitoring Toolkitcited by 1
  15. 2024A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsiscited by 1
  16. 2025From Source to Target: Leveraging Transfer Learning for Predictive Process Monitoring in Organizationscited by 1
  17. 2021A Systematic Literature Review on Process-Aware Recommender Systemscited by 0
  18. 2021ProcK: Machine Learning for Knowledge-Intensive Processescited by 0
  19. 2022Clustering-based Aggregations for Prediction in Event Streamscited by 0
  20. 2023Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation stylescited by 0
  21. 2023Measuring the Stability of Process Outcome Predictions in Online Settingscited by 0
  22. 2024Structural Positional Encoding for knowledge integration in transformer-based medical process monitoringcited by 0
  23. 2024Extending predictive process monitoring for collaborative processescited by 0
  24. 2025SHAining on Process Mining: Explaining Event Log Characteristics Impact on Algorithmscited by 0
  25. 2025Auto-ML Graph Neural Network Hypermodels for Outcome Prediction in Event-Sequence Datacited by 0