mlsanityData to 7 Oct 2026

Prescriptive Process Monitoring: Quo Vadis?

arXiv 2112.01769 ↗First posted 3 Dec 2021cs.AI cs.LGcited by 15

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Abstract (arXiv)

Prescriptive process monitoring methods seek to optimize a business process by recommending interventions at runtime to prevent negative outcomes or poorly performing cases. In recent years, various prescriptive process monitoring methods have been proposed. This paper studies existing methods in this field via a Systematic Literature Review (SLR). In order to structure the field, the paper proposes a framework for characterizing prescriptive process monitoring methods according to their performance objective, performance metrics, intervention types, modeling techniques, data inputs, and intervention policies. The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods. The paper highlights the need to validate existing and new methods in real-world settings, to extend the types of interventions beyond those related to the temporal and cost perspectives, and to design policies that take into account causality and second-order effects.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 12 of its 58 references that are in the map. First posted on arXiv 3 Dec 2021.

Builds on

12 of 58 references.

References · 12

  1. 2013Predictive Monitoring of Business Processescited by 37
  2. 2018Predictive Process Monitoring Methods: Which One Suits Me Best?cited by 27
  3. 2020Prescriptive Business Process Monitoring for Recommending Next Best Actionscited by 22
  4. 2019Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoringcited by 18
  5. 2021Prescriptive Process Monitoring for Cost-Aware Cycle Time Reductioncited by 17
  6. 2018Alarm-Based Prescriptive Process Monitoringcited by 16
  7. 2021DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approachcited by 13
  8. 2021Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approachcited by 12
  9. 2018DeepProcess: Supporting business process execution using a MANN-based recommender systemcited by 6
  10. 2019What if Process Predictions are not followed by Good Recommendations? (Technical Report)cited by 3
  11. 2018A Comparative Evaluation of Log-Based Process Performance Analysis Techniquescited by 1
  12. 2021Automatic Resource Allocation in Business Processes: A Systematic Literature Surveycited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 5; 2024: 2; 2025: 3; 2026: 1.

15 papers.

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  1. 2024Machine Unlearning: A Comprehensive Surveycited by 33
  2. 2022Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patternscited by 7
  3. 2022Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestrationcited by 6
  4. 2023Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learningcited by 6
  5. 2022Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processescited by 5
  6. 2023Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approachescited by 5
  7. 2023Learning policies for resource allocation in business processescited by 4
  8. 2023Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoringcited by 4
  9. 2023Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approachcited by 4
  10. 2025SimBank: from Simulation to Solution in Prescriptive Process Monitoringcited by 4
  11. 2024Recent Advances in Data-Driven Business Process Managementcited by 2
  12. 2022Learning to act: a Reinforcement Learning approach to recommend the best next activitiescited by 1
3 more
  1. 2025A Rollout-Based Algorithm and Reward Function for Resource Allocation in Business Processescited by 0
  2. 2025SCOPE: Sequential Causal Optimization of Process Interventionscited by 0
  3. 2026Supporting Autonomous Process Execution within a Multi-Perspective Constraint Frame via Numeric Planningcited by 0