2025/01/17 by Francesco Vitale, Marco Pegoraro, Wil M. P. van der Aalst +1 · 1 voice · 1 citation
Business, Management and Accounting · Computer Science · #Anomaly Detection Techniques and Applications #Business Process Modeling and Analysis #Software Engineering Research
paper · doi:10.1016/j.knosys.2025.112970
openalex publication_date 2025/01/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The business processes of organizations may deviate from normal control flow due to disruptive anomalies, including unknown, skipped, and wrongly-ordered activities. To identify these control-flow anomalies, process mining can check control-flow correctness against a reference process model through conformance checking, an explainable set of algorithms that allows linking any deviations with model elements. However, the effectiveness of conformance checking-based techniques is negatively affected by noisy event data and low-quality process models. To address these shortcomings and support the development of competitive and explainable conformance checking-based techniques for control-flow anomaly detection , we propose a novel process mining-based feature extraction approach with alignment-based conformance checking. This variant aligns the deviating control flow with a reference process model; the resulting alignment can be inspected to extract additional statistics such as the number of times a given activity caused mismatches. We integrate this approach into a flexible and explainable framework for developing techniques for control-flow anomaly detection . The framework combines process mining-based feature extraction and dimensionality reduction to handle high-dimensional feature sets, achieve detection effectiveness, and support explainability. The results show that the framework techniques implementing our approach outperform the baseline conformance checking-based techniques while maintaining the explainable nature of conformance checking. We also provide an explanation of why existing conformance checking-based techniques may be ineffective. Finally, the results indicate that detection effectiveness is not solely dependent on the specific framework technique used, as no one-size-fits-all process mining-based feature extraction approach is suitable for all the synthetic and real-world datasets.