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
- 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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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 |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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application/pdf |
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reponame:CIC Digital (CICBA) instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires instacron:CICBA |
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Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
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CICBA |
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CICBA |
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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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