Causal Learning: Monitoring Business Processes Based on Causal Structures

Vista sencilla de ítem
dc.contributor.authorMontoya, Fernando
dc.contributor.authorAstudillo, Hernán
dc.contributor.authorDíaz, Daniela
dc.contributor.authorBerríos, Esteban
dc.date.accessioned2024-11-11T12:12:11Z
dc.date.available2024-11-11T12:12:11Z
dc.date.issued2024-10
dc.descriptionIndexación: Scopus.
dc.description.abstractConventional 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.
dc.description.urihttps://www-scopus-com.recursosbiblioteca.unab.cl/record/display.uri?eid=2-s2.0-85207681640&origin=resultslist&sort=plf-f&src=s&sid=f06abd21ed9fd72fccf5b29afb02c858&sot=aff&sdt=cl&cluster=scosubjabbr%2C%22PHYS%22%2Ct%2C%22EART%22%2Ct%2C%22CHEM%22%2Ct%2C%22ENGI%22%2Ct%2C%22MATE%22%2Ct%2Bscolang%2C%22English%22%2Ct%2Bscofreetoread%2C%22all%22%2Ct&s=AF-ID%2860002636%29+AND+SUBJAREA%28PHYS%29&sl=34&sessionSearchId=f06abd21ed9fd72fccf5b29afb02c858&relpos=5
dc.identifier.citationEntropy Open Access Volume 26, Issue 10 October 2024 Article number 867
dc.identifier.doi10.3390/e26100867
dc.identifier.issn1099-4300
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/61843
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights.licenseCC BY 4.0 Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBusiness process mining
dc.subjectBusiness process monitoring
dc.subjectCausal attribution of anomalies
dc.subjectCausal attribution of distributional change
dc.subjectCausal graph
dc.titleCausal Learning: Monitoring Business Processes Based on Causal Structures
dc.typeArtículo
Archivos
Bloque original
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
Causal-Learning-Monitoring-Business-Processes-Based-on-Causal-Structures.pdf
Tamaño:
591.86 KB
Formato:
Adobe Portable Document Format
Descripción:
TEXTO COMPLETO EN INGLÉS
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar
Colecciones
FM - Artículos de Revista