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Predictive Process Monitoring Methods: Which One Suits Me Best?

arXiv 1804.02422 ↗First posted 6 Apr 2018cs.AIcited by 27

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Abstract (arXiv)

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 6; 2023: 6; 2024: 1; 2025: 6; 2026: 1.

27 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: Quo Vadis?cited by 15
  3. 2022Counterfactual Explanations for Predictive Business Process Monitoringcited by 12
  4. 2023Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspectivecited by 10
  5. 2021How do I update my model? On the resilience of Predictive Process Monitoring models to changecited by 7
  6. 2021Evaluating Predictive Business Process Monitoring Approaches on Small Event Logscited by 6
  7. 2022Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestrationcited by 6
  8. 2023Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approachescited by 5
  9. 2024Machine learning in business process management: A systematic literature reviewcited by 5
  10. 2021Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Reportcited by 4
  11. 2022Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directionscited by 4
  12. 2023Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoringcited by 4
15 more
  1. 2023Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Predictioncited by 4
  2. 2022Explainable Predictive Process Monitoring: A User Evaluationcited by 3
  3. 2023A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areascited by 2
  4. 2025An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoringcited by 2
  5. 2021Process Model Forecasting Using Time Series Analysis of Event Sequence Datacited by 1
  6. 2022Learning to act: a Reinforcement Learning approach to recommend the best next activitiescited by 1
  7. 2022Nirdizati: an Advanced Predictive Process Monitoring Toolkitcited by 1
  8. 2025Log Optimization Simplification Method for Predicting Remaining Timecited by 1
  9. 2025From Source to Target: Leveraging Transfer Learning for Predictive Process Monitoring in Organizationscited by 1
  10. 2021A Systematic Literature Review on Process-Aware Recommender Systemscited by 0
  11. 2023Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation stylescited by 0
  12. 2025Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatmentcited by 0
  13. 2025Working My Way Back to You: Resource-Centric Next-Activity Predictioncited by 0
  14. 2025How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestampscited by 0
  15. 2026David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLMcited by 0