Causal Learning: Monitoring Business Processes Based on Causal Structures
No hay miniatura disponible
Archivos
Fecha
2024-10
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
Multidisciplinary Digital Publishing Institute (MDPI)
Nombre de Curso
Licencia CC
CC BY 4.0 Attribution 4.0 International
Licencia CC
https://creativecommons.org/licenses/by/4.0/
Resumen
Conventional methods for process monitoring often fail to capture the causal relationships that drive outcomes, making hard to distinguish causal anomalies from mere correlations in activity flows. Hence, there is a need for approaches that allow causal interpretation of atypical scenarios (anomalies), allowing to identify the influence of operational variables on these anomalies. This article introduces (CaProM), an innovative technique based on causality techniques, applied during the planning phase in business process environments. The technique combines two causal perspectives: anomaly attribution and distribution change attribution. It has three stages: (1) process events are collected and recorded, identifying flow instances; (2) causal learning of process activities, building a directed acyclic graphs (DAGs) represent dependencies among variables; and (3) use of DAGs to monitor the process, detecting anomalies and critical nodes. The technique was validated with a industry dataset from the banking sector, comprising 562 activity flow plans. The study monitored causal structures during the planning and execution stages, and allowed to identify the main factor behind a major deviation from planned values. This work contributes to business process monitoring by introducing a causal approach that enhances both the interpretability and explainability of anomalies. The technique allows to understand which specific variables have caused an atypical scenario, providing a clear view of the causal relationships within processes and ensuring greater accuracy in decision-making. This causal analysis employs cross-sectional data, avoiding the need to average multiple time instances and reducing potential biases, and unlike time series methods, it preserves the relationships among variables.
Notas
Indexación: Scopus.
Palabras clave
Business process mining, Business process monitoring, Causal attribution of anomalies, Causal attribution of distributional change, Causal graph
Citación
Entropy Open Access Volume 26, Issue 10 October 2024 Article number 867
DOI
10.3390/e26100867