mlsanityData to 7 Oct 2026

An Explainable Decision Support System for Predictive Process Analytics

arXiv 2207.12782 ↗First posted 26 Jul 2022cs.LGcited by 8

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Abstract (arXiv)

Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way. Otherwise, they will be unlikely to trust the predictive monitoring technology and, hence, adopt it. This paper proposes a predictive analytics framework that is also equipped with explanation capabilities based on the game theory of Shapley Values. The framework has been implemented in the IBM Process Mining suite and commercialized for business users. The framework has been tested on real-life event data to assess the quality of the predictions and the corresponding evaluations. In particular, a user evaluation has been performed in order to understand if the explanations provided by the system were intelligible to process stakeholders.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 24 of its 57 references that are in the map. First posted on arXiv 26 Jul 2022.

Builds on

24 of 57 references.

References · 24

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  12. 2020A systematic review and taxonomy of explanations in decision support and recommender systemscited by 45
12 more
  1. 2020Explainable Predictive Process Monitoringcited by 27
  2. 2017LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instancescited by 19
  3. 2021DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approachcited by 13
  4. 2020XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRPcited by 12
  5. 2022Counterfactual Explanations for Predictive Business Process Monitoringcited by 12
  6. 2022A Meta Survey of Quality Evaluation Criteria in Explanation Methodscited by 8
  7. 2021Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanismscited by 6
  8. 2020Cause vs. Effect in Context-Sensitive Prediction of Business Process Instancescited by 5
  9. 2021The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directionscited by 3
  10. 2022Explainable Predictive Process Monitoring: A User Evaluationcited by 3
  11. 2022Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?cited by 3
  12. 2022XAI in the context of Predictive Process Monitoring: Too much to Revealcited by 1

Built on by

Cited by year (2026 to 7 Oct)

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

8 papers.

  1. 2024How well can a large language model explain business processes as perceived by users?cited by 8
  2. 2024SNAP: Semantic Stories for Next Activity Predictioncited by 8
  3. 2022Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patternscited by 7
  4. 2023Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Reviewcited by 3
  5. 2024Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoringcited by 3
  6. 2025The WHY in Business Processes: Unification of Causal Process Modelscited by 1
  7. 2025The Role of Explanation Styles and Perceived Accuracy on Decision Making in Predictive Process Monitoringcited by 0
  8. 2026Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logscited by 0