Methods for concept analysis and multi-relational data mining: a systematic literature review

Autores
Leutwyler, Nicolás; Lezoche, Mario; Franciosi, Chiara; Panetto, Hervé; Teste, Laurent; Torres, Diego
Año de publicación
2024
Idioma
inglés
Tipo de recurso
reseña artículo
Estado
versión publicada
Descripción
The Internet of Things massive adoption in many industrial areas in addition to the requirement of modern services is posing huge challenges to the field of data mining. Moreover, the semantic interoperability of systems and enterprises requires to operate between many different formats such as ontologies, knowledge graphs, or relational databases, as well as different contexts such as static, dynamic, or real time. Consequently, supporting this semantic interoperability requires a wide range of knowledge discovery methods with different capabilities that answer to the context of distributed architectures (DA). However, to the best of our knowledge there is no general review in recent time about the state of the art of Concept Analysis (CA) and multi-relational data mining (MRDM) methods regarding knowledge discovery in DA considering semantic interoperability. In this work, a systematic literature review on CA and MRDM is conducted, providing a discussion on the characteristics they have according to the papers reviewed, supported by a clusterization technique based on association rules. Moreover, the review allowed the identification of three research gaps toward a more scalable set of methods in the context of DA and heterogeneous sources.
Materia
Ciencias de la Computación e Información
Knowledge extraction
Knowledge discovery
Concept analysis
Multi-relational
Semantic interoperability
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-nd/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/12253

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network_name_str CIC Digital (CICBA)
spelling Methods for concept analysis and multi-relational data mining: a systematic literature reviewLeutwyler, NicolásLezoche, MarioFranciosi, ChiaraPanetto, HervéTeste, LaurentTorres, DiegoCiencias de la Computación e InformaciónKnowledge extractionKnowledge discoveryConcept analysisMulti-relationalSemantic interoperabilityThe Internet of Things massive adoption in many industrial areas in addition to the requirement of modern services is posing huge challenges to the field of data mining. Moreover, the semantic interoperability of systems and enterprises requires to operate between many different formats such as ontologies, knowledge graphs, or relational databases, as well as different contexts such as static, dynamic, or real time. Consequently, supporting this semantic interoperability requires a wide range of knowledge discovery methods with different capabilities that answer to the context of distributed architectures (DA). However, to the best of our knowledge there is no general review in recent time about the state of the art of Concept Analysis (CA) and multi-relational data mining (MRDM) methods regarding knowledge discovery in DA considering semantic interoperability. In this work, a systematic literature review on CA and MRDM is conducted, providing a discussion on the characteristics they have according to the papers reviewed, supported by a clusterization technique based on association rules. Moreover, the review allowed the identification of three research gaps toward a more scalable set of methods in the context of DA and heterogeneous sources.2024info:eu-repo/semantics/reviewinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_ba08info:ar-repo/semantics/revisionLiterariaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/12253enginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s10115-024-02139-xinfo:eu-repo/semantics/altIdentifier/issn/0219-3116info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:20Zoai:digital.cic.gba.gob.ar:11746/12253Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:40:20.801CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Methods for concept analysis and multi-relational data mining: a systematic literature review
title Methods for concept analysis and multi-relational data mining: a systematic literature review
spellingShingle Methods for concept analysis and multi-relational data mining: a systematic literature review
Leutwyler, Nicolás
Ciencias de la Computación e Información
Knowledge extraction
Knowledge discovery
Concept analysis
Multi-relational
Semantic interoperability
title_short Methods for concept analysis and multi-relational data mining: a systematic literature review
title_full Methods for concept analysis and multi-relational data mining: a systematic literature review
title_fullStr Methods for concept analysis and multi-relational data mining: a systematic literature review
title_full_unstemmed Methods for concept analysis and multi-relational data mining: a systematic literature review
title_sort Methods for concept analysis and multi-relational data mining: a systematic literature review
dc.creator.none.fl_str_mv Leutwyler, Nicolás
Lezoche, Mario
Franciosi, Chiara
Panetto, Hervé
Teste, Laurent
Torres, Diego
author Leutwyler, Nicolás
author_facet Leutwyler, Nicolás
Lezoche, Mario
Franciosi, Chiara
Panetto, Hervé
Teste, Laurent
Torres, Diego
author_role author
author2 Lezoche, Mario
Franciosi, Chiara
Panetto, Hervé
Teste, Laurent
Torres, Diego
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias de la Computación e Información
Knowledge extraction
Knowledge discovery
Concept analysis
Multi-relational
Semantic interoperability
topic Ciencias de la Computación e Información
Knowledge extraction
Knowledge discovery
Concept analysis
Multi-relational
Semantic interoperability
dc.description.none.fl_txt_mv The Internet of Things massive adoption in many industrial areas in addition to the requirement of modern services is posing huge challenges to the field of data mining. Moreover, the semantic interoperability of systems and enterprises requires to operate between many different formats such as ontologies, knowledge graphs, or relational databases, as well as different contexts such as static, dynamic, or real time. Consequently, supporting this semantic interoperability requires a wide range of knowledge discovery methods with different capabilities that answer to the context of distributed architectures (DA). However, to the best of our knowledge there is no general review in recent time about the state of the art of Concept Analysis (CA) and multi-relational data mining (MRDM) methods regarding knowledge discovery in DA considering semantic interoperability. In this work, a systematic literature review on CA and MRDM is conducted, providing a discussion on the characteristics they have according to the papers reviewed, supported by a clusterization technique based on association rules. Moreover, the review allowed the identification of three research gaps toward a more scalable set of methods in the context of DA and heterogeneous sources.
description The Internet of Things massive adoption in many industrial areas in addition to the requirement of modern services is posing huge challenges to the field of data mining. Moreover, the semantic interoperability of systems and enterprises requires to operate between many different formats such as ontologies, knowledge graphs, or relational databases, as well as different contexts such as static, dynamic, or real time. Consequently, supporting this semantic interoperability requires a wide range of knowledge discovery methods with different capabilities that answer to the context of distributed architectures (DA). However, to the best of our knowledge there is no general review in recent time about the state of the art of Concept Analysis (CA) and multi-relational data mining (MRDM) methods regarding knowledge discovery in DA considering semantic interoperability. In this work, a systematic literature review on CA and MRDM is conducted, providing a discussion on the characteristics they have according to the papers reviewed, supported by a clusterization technique based on association rules. Moreover, the review allowed the identification of three research gaps toward a more scalable set of methods in the context of DA and heterogeneous sources.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/review
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_ba08
info:ar-repo/semantics/revisionLiteraria
format review
status_str publishedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/12253
url https://digital.cic.gba.gob.ar/handle/11746/12253
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s10115-024-02139-x
info:eu-repo/semantics/altIdentifier/issn/0219-3116
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
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