mlsanityData to 7 Oct 2026

Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions

arXiv 2205.05446 ↗First posted 10 May 2022cs.SE cs.AIcited by 4

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Abstract (arXiv)

Business process compliance is a key area of business process management and aims at ensuring that processes obey to compliance constraints such as regulatory constraints or business rules imposed on them. Process compliance can be checked during process design time based on verification of process models and at runtime based on monitoring the compliance states of running process instances. For existing compliance monitoring approaches it remains unclear whether and how compliance violations can be predicted, although predictions are crucial in order to prepare and take countermeasures in time. This work, hence, analyzes existing literature from compliance monitoring as well as predictive process monitoring and provides an updated framework of compliance monitoring functionalities. Moreover, it raises the vision of a comprehensive predictive compliance monitoring system that integrates existing predicate prediction approaches with the idea of employing PPM with different prediction goals such as next activity or remaining time for prediction and subsequent mapping of the prediction results onto the given set of compliance constraints (PCM). For each compliance monitoring functionality we elicit PCM system requirements and assess their coverage by existing approaches. Based on the assessment, open challenges and research directions realizing a comprehensive PCM system are elaborated.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 32 of its 194 references that are in the map. First posted on arXiv 10 May 2022.

Builds on

32 of 194 references.

References · 32

  1. 2017A Survey on Multi-Task Learningcited by 838
  2. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 536
  3. 2020A Survey on the Explainability of Supervised Machine Learningcited by 225
  4. 2016Predicting Process Behaviour using Deep Learningcited by 59
  5. 2020Deep Learning for Predictive Business Process Monitoring: Review and Benchmarkcited by 45
  6. 2018Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoringcited by 43
  7. 2018Deep Learning with Long Short-Term Memory for Time Series Predictioncited by 39
  8. 2013Predictive Monitoring of Business Processescited by 37
  9. 2017Outcome-Oriented Predictive Process Monitoring: Review and Benchmarkcited by 37
  10. 2021A systematic literature review on state-of-the-art deep learning methods for process predictioncited by 32
  11. 2018Predictive Process Monitoring Methods: Which One Suits Me Best?cited by 27
  12. 2020Explainable Predictive Process Monitoringcited by 27
20 more
  1. 2015Clustering-Based Predictive Process Monitoringcited by 24
  2. 2016Time and Activity Sequence Prediction of Business Process Instancescited by 24
  3. 2020Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoringcited by 20
  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. 2021Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approachcited by 12
  7. 2021Text-Aware Predictive Monitoring of Business Processescited by 9
  8. 2020Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturingcited by 7
  9. 2021How do I update my model? On the resilience of Predictive Process Monitoring models to changecited by 7
  10. 2021Evaluating Predictive Business Process Monitoring Approaches on Small Event Logscited by 6
  11. 2017Blockchains for Business Process Management - Challenges and Opportunitiescited by 5
  12. 2020Cause vs. Effect in Context-Sensitive Prediction of Business Process Instancescited by 5
  13. 2021Verifying Compliance in Process Choreographies: Foundations, Algorithms, and Implementationcited by 4
  14. 2019On Observability and Monitoring of Distributed Systems: An Industry Interview Studycited by 3
  15. 2022Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?cited by 3
  16. 2017Event-based Failure Prediction in Distributed Business Processescited by 2
  17. 2016Collecting Examples for Instance-Spanning Constraintscited by 1
  18. 2018Untangling the GDPR Using ConRelMinercited by 1
  19. 2019Specification-Driven Predictive Business Process Monitoringcited by 1
  20. 2021Generating Reliable Process Event Streams and Time Series Data based on Neural Networkscited by 1

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

4 papers.