Artificial Intelligence Methodologies for Data Management

This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the ma...

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Autores principales:	Joel Serey, Luis Quezada, Miguel Alfaro, Guillermo Fuertes, Manuel Vargas, Rodrigo Ternero, Jorge Sabattin, Claudia Duran, Sebastian Gutierrez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning data management artificial intelligence big data Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/5aa0c8095c7749249c66e6e3fa11e45b
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id	oai:doaj.org-article:5aa0c8095c7749249c66e6e3fa11e45b
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spelling	oai:doaj.org-article:5aa0c8095c7749249c66e6e3fa11e45b2021-11-25T19:06:15ZArtificial Intelligence Methodologies for Data Management10.3390/sym131120402073-8994https://doaj.org/article/5aa0c8095c7749249c66e6e3fa11e45b2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2040https://doaj.org/toc/2073-8994This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the main methodological contributions of artificial intelligence through machine learning. The methodology used to study the documents was content analysis; the basic terminology of the study corresponds to machine learning, artificial intelligence, and big data between the years 2017 and 2021. For this study, we selected 181 references, of which 120 are part of the literature review. The conceptual framework includes 12 categories, four groups, and eight subgroups. The study of data management using AI methodologies presents symmetry in the four machine learning groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. Furthermore, the artificial intelligence methods with more symmetry in all groups are artificial neural networks, Support Vector Machines, K-means, and Bayesian Methods. Finally, five research avenues are presented to improve the prediction of machine learning.Joel SereyLuis QuezadaMiguel AlfaroGuillermo FuertesManuel VargasRodrigo TerneroJorge SabattinClaudia DuranSebastian GutierrezMDPI AGarticlemachine learningdata managementartificial intelligencebig dataMathematicsQA1-939ENSymmetry, Vol 13, Iss 2040, p 2040 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning data management artificial intelligence big data Mathematics QA1-939
spellingShingle	machine learning data management artificial intelligence big data Mathematics QA1-939 Joel Serey Luis Quezada Miguel Alfaro Guillermo Fuertes Manuel Vargas Rodrigo Ternero Jorge Sabattin Claudia Duran Sebastian Gutierrez Artificial Intelligence Methodologies for Data Management
description	This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the main methodological contributions of artificial intelligence through machine learning. The methodology used to study the documents was content analysis; the basic terminology of the study corresponds to machine learning, artificial intelligence, and big data between the years 2017 and 2021. For this study, we selected 181 references, of which 120 are part of the literature review. The conceptual framework includes 12 categories, four groups, and eight subgroups. The study of data management using AI methodologies presents symmetry in the four machine learning groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. Furthermore, the artificial intelligence methods with more symmetry in all groups are artificial neural networks, Support Vector Machines, K-means, and Bayesian Methods. Finally, five research avenues are presented to improve the prediction of machine learning.
format	article
author	Joel Serey Luis Quezada Miguel Alfaro Guillermo Fuertes Manuel Vargas Rodrigo Ternero Jorge Sabattin Claudia Duran Sebastian Gutierrez
author_facet	Joel Serey Luis Quezada Miguel Alfaro Guillermo Fuertes Manuel Vargas Rodrigo Ternero Jorge Sabattin Claudia Duran Sebastian Gutierrez
author_sort	Joel Serey
title	Artificial Intelligence Methodologies for Data Management
title_short	Artificial Intelligence Methodologies for Data Management
title_full	Artificial Intelligence Methodologies for Data Management
title_fullStr	Artificial Intelligence Methodologies for Data Management
title_full_unstemmed	Artificial Intelligence Methodologies for Data Management
title_sort	artificial intelligence methodologies for data management
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5aa0c8095c7749249c66e6e3fa11e45b
work_keys_str_mv	AT joelserey artificialintelligencemethodologiesfordatamanagement AT luisquezada artificialintelligencemethodologiesfordatamanagement AT miguelalfaro artificialintelligencemethodologiesfordatamanagement AT guillermofuertes artificialintelligencemethodologiesfordatamanagement AT manuelvargas artificialintelligencemethodologiesfordatamanagement AT rodrigoternero artificialintelligencemethodologiesfordatamanagement AT jorgesabattin artificialintelligencemethodologiesfordatamanagement AT claudiaduran artificialintelligencemethodologiesfordatamanagement AT sebastiangutierrez artificialintelligencemethodologiesfordatamanagement
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Artificial Intelligence Methodologies for Data Management

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