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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.01141 |
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| _version_ | 1866916595071188992 |
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| author | Chen, Qian Rinderle-Ma, Stefanie Wen, Lijie |
| author_facet | Chen, Qian Rinderle-Ma, Stefanie Wen, Lijie |
| contents | Most existing process compliance monitoring approaches detect compliance violations in an ex post manner. Only predicate prediction focuses on predicting them. However, predicate prediction provides a binary yes/no notion of compliance, lacking the ability to measure to which extent an ongoing process instance deviates from the desired state as specified in constraints. Here, being able to quantify the magnitude of violation would provide organizations with deeper insights into their operational performance, enabling informed decision making to reduce or mitigate the risk of non-compliance. Thus, we propose two predictive compliance monitoring approaches to close this research gap. The first approach reformulates the binary classification problem as a hybrid task that considers both classification and regression, while the second employs a multi-task learning method to explicitly predict the compliance status and the magnitude of violation for deviant cases simultaneously. In this work, we focus on temporal constraints as they are significant in almost any application domain, e.g., health care. The evaluation on synthetic and real-world event logs demonstrates that our approaches are capable of quantifying the magnitude of violations while maintaining comparable performance for compliance predictions achieved by state-of-the-art approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Yes or No: Predictive Compliance Monitoring Approaches for Quantifying the Magnitude of Compliance Violations Chen, Qian Rinderle-Ma, Stefanie Wen, Lijie Machine Learning Artificial Intelligence Most existing process compliance monitoring approaches detect compliance violations in an ex post manner. Only predicate prediction focuses on predicting them. However, predicate prediction provides a binary yes/no notion of compliance, lacking the ability to measure to which extent an ongoing process instance deviates from the desired state as specified in constraints. Here, being able to quantify the magnitude of violation would provide organizations with deeper insights into their operational performance, enabling informed decision making to reduce or mitigate the risk of non-compliance. Thus, we propose two predictive compliance monitoring approaches to close this research gap. The first approach reformulates the binary classification problem as a hybrid task that considers both classification and regression, while the second employs a multi-task learning method to explicitly predict the compliance status and the magnitude of violation for deviant cases simultaneously. In this work, we focus on temporal constraints as they are significant in almost any application domain, e.g., health care. The evaluation on synthetic and real-world event logs demonstrates that our approaches are capable of quantifying the magnitude of violations while maintaining comparable performance for compliance predictions achieved by state-of-the-art approaches. |
| title | Beyond Yes or No: Predictive Compliance Monitoring Approaches for Quantifying the Magnitude of Compliance Violations |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.01141 |