DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking

Conformance checking is a crucial aspect of process mining, enabling organizations to identify deviations between actual process behavior and modeled expectations. At the heart of conformance checking lies the concept of optimal alignments, which provide a detailed, cost-minimized mapping of observe...

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Detalles Bibliográficos
Autores: Casas Ramos, Jacobo, Lama Penín, Manuel, Mucientes Molina, Manuel
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Santiago de Compostela (USC)
Repositorio:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglés
OAI Identifier:oai:minerva.usc.gal:10347/43349
Acceso en línea:https://hdl.handle.net/10347/43349
Access Level:acceso abierto
Palabra clave:Process mining
Conformance checking
Optimal alignments
Declarative process models
120304 Inteligencia artificial
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spelling DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checkingCasas Ramos, JacoboLama Penín, ManuelMucientes Molina, ManuelProcess miningConformance checkingOptimal alignmentsDeclarative process models120304 Inteligencia artificialConformance checking is a crucial aspect of process mining, enabling organizations to identify deviations between actual process behavior and modeled expectations. At the heart of conformance checking lies the concept of optimal alignments, which provide a detailed, cost-minimized mapping of observed behavior to expected behavior. Optimal alignments facilitate the identification of root causes of non-conformity and guide corrective actions. This is a critical area where Artificial Intelligence (AI) plays a pivotal role in driving effective process improvement. However, computing optimal alignments poses significant computational challenges due to the vast search space inherent in declarative process models. Consequently, existing approaches often struggle with scalability and efficiency, limiting their applicability in real-world settings. This paper introduces DeclareAligner, a novel algorithm that uses the A* search algorithm, an established AI pathfinding technique, to tackle the problem from a fresh perspective leveraging the flexibility of declarative models. Key features of DeclareAligner include only performing actions that actively contribute to fixing constraint violations, utilizing a tailored heuristic to navigate towards optimal solutions, and employing early pruning to eliminate unproductive branches, while also streamlining the process through preprocessing and consolidating multiple fixes into unified actions. The proposed method is evaluated using 8054 synthetic and real-life alignment problems, demonstrating its ability to efficiently compute optimal alignments by significantly outperforming the current state of the art. By enabling process analysts to more effectively identify and understand conformance issues, DeclareAligner has the potential to drive meaningful process improvement and management.ElsevierUniversidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)Universidade de Santiago de Compostela. Departamento de Electrónica e Computación20252025-07-3120252025-07-31journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10347/43349reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostelainstname:Universidad de Santiago de Compostela (USC)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-149549NB-I00 APROVECHANDO LA INTELIGENCIA ARTIFICIAL PARA UNA MO-NITORIZACION PREDICTIVA ROBUSTA EN MINERIA DE PROCESOSAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Proyectos de transición ecológica y transición digital 2021 TED2021-130374B-C21 MONITORIZACION PREDICTIVA Y CAUSALIDAD PARA REHABILITACION CARDIACAopen accesshttp://purl.org/coar/access_right/c_abf2© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by- nc-nd/4.0/ ).http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:minerva.usc.gal:10347/433492026-06-15T12:47:27Z
dc.title.none.fl_str_mv DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking
title DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking
spellingShingle DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking
Casas Ramos, Jacobo
Process mining
Conformance checking
Optimal alignments
Declarative process models
120304 Inteligencia artificial
title_short DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking
title_full DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking
title_fullStr DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking
title_full_unstemmed DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking
title_sort DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking
dc.creator.none.fl_str_mv Casas Ramos, Jacobo
Lama Penín, Manuel
Mucientes Molina, Manuel
author Casas Ramos, Jacobo
author_facet Casas Ramos, Jacobo
Lama Penín, Manuel
Mucientes Molina, Manuel
author_role author
author2 Lama Penín, Manuel
Mucientes Molina, Manuel
author2_role author
author
dc.contributor.none.fl_str_mv Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
dc.subject.none.fl_str_mv Process mining
Conformance checking
Optimal alignments
Declarative process models
120304 Inteligencia artificial
topic Process mining
Conformance checking
Optimal alignments
Declarative process models
120304 Inteligencia artificial
description Conformance checking is a crucial aspect of process mining, enabling organizations to identify deviations between actual process behavior and modeled expectations. At the heart of conformance checking lies the concept of optimal alignments, which provide a detailed, cost-minimized mapping of observed behavior to expected behavior. Optimal alignments facilitate the identification of root causes of non-conformity and guide corrective actions. This is a critical area where Artificial Intelligence (AI) plays a pivotal role in driving effective process improvement. However, computing optimal alignments poses significant computational challenges due to the vast search space inherent in declarative process models. Consequently, existing approaches often struggle with scalability and efficiency, limiting their applicability in real-world settings. This paper introduces DeclareAligner, a novel algorithm that uses the A* search algorithm, an established AI pathfinding technique, to tackle the problem from a fresh perspective leveraging the flexibility of declarative models. Key features of DeclareAligner include only performing actions that actively contribute to fixing constraint violations, utilizing a tailored heuristic to navigate towards optimal solutions, and employing early pruning to eliminate unproductive branches, while also streamlining the process through preprocessing and consolidating multiple fixes into unified actions. The proposed method is evaluated using 8054 synthetic and real-life alignment problems, demonstrating its ability to efficiently compute optimal alignments by significantly outperforming the current state of the art. By enabling process analysts to more effectively identify and understand conformance issues, DeclareAligner has the potential to drive meaningful process improvement and management.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-07-31
2025
2025-07-31
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10347/43349
url https://hdl.handle.net/10347/43349
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-149549NB-I00 APROVECHANDO LA INTELIGENCIA ARTIFICIAL PARA UNA MO-NITORIZACION PREDICTIVA ROBUSTA EN MINERIA DE PROCESOS
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Proyectos de transición ecológica y transición digital 2021 TED2021-130374B-C21 MONITORIZACION PREDICTIVA Y CAUSALIDAD PARA REHABILITACION CARDIACA
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname:Universidad de Santiago de Compostela (USC)
instname_str Universidad de Santiago de Compostela (USC)
reponame_str Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
collection Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
repository.name.fl_str_mv
repository.mail.fl_str_mv
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