Data science methodologies selection with hierarchical analytical process and personal construction theory

Autores
Eckert, Karina; Britos, Paola Verónica
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The amount of data currently available for Strategic Decision Making is substantial; which is why Data Science find itself in apogee in various areas where it can be applied. Expertise respecting the areas’ methodologies is fundamental; which is why, the objective of this paper is to compare and ponder them, for which, Analytic Hierarchy Process, was utilized along with linguistic tags and Personal Construction Theory, with the purpose of establishing and prioritizing characteristics according to their degree of compliance in real validation cases. The sub-criteria were grouped in different levels, conforming a hierarchy for the present problem. The validation case consisted in determining causes for breakdowns in new automobiles as they are being transported from the factory to the concessionaires; in which the proposed model proved useful and MoProPEI could be identified as the most adequate methodology.
XVI Workshop Bases de Datos y Minería de Datos.
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
Data Science Methodologies
Analytic Hierarchy Process
Personal Construction Theory
Linguistic tags
Criteria
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/91028
Acceder
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network_name_str SEDICI (UNLP)
spelling Data science methodologies selection with hierarchical analytical process and personal construction theoryEckert, KarinaBritos, Paola VerónicaCiencias InformáticasData Science MethodologiesAnalytic Hierarchy ProcessPersonal Construction TheoryLinguistic tagsCriteriaThe amount of data currently available for Strategic Decision Making is substantial; which is why Data Science find itself in apogee in various areas where it can be applied. Expertise respecting the areas’ methodologies is fundamental; which is why, the objective of this paper is to compare and ponder them, for which, Analytic Hierarchy Process, was utilized along with linguistic tags and Personal Construction Theory, with the purpose of establishing and prioritizing characteristics according to their degree of compliance in real validation cases. The sub-criteria were grouped in different levels, conforming a hierarchy for the present problem. The validation case consisted in determining causes for breakdowns in new automobiles as they are being transported from the factory to the concessionaires; in which the proposed model proved useful and MoProPEI could be identified as the most adequate methodology.XVI Workshop Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informática2019-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf477-486http://sedici.unlp.edu.ar/handle/10915/91028enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-688-377-1info:eu-repo/semantics/reference/hdl/10915/90359info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:10:57Zoai:sedici.unlp.edu.ar:10915/91028Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:10:57.486SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Data science methodologies selection with hierarchical analytical process and personal construction theory
title Data science methodologies selection with hierarchical analytical process and personal construction theory
spellingShingle Data science methodologies selection with hierarchical analytical process and personal construction theory
Eckert, Karina
Ciencias Informáticas
Data Science Methodologies
Analytic Hierarchy Process
Personal Construction Theory
Linguistic tags
Criteria
title_short Data science methodologies selection with hierarchical analytical process and personal construction theory
title_full Data science methodologies selection with hierarchical analytical process and personal construction theory
title_fullStr Data science methodologies selection with hierarchical analytical process and personal construction theory
title_full_unstemmed Data science methodologies selection with hierarchical analytical process and personal construction theory
title_sort Data science methodologies selection with hierarchical analytical process and personal construction theory
dc.creator.none.fl_str_mv Eckert, Karina
Britos, Paola Verónica
author Eckert, Karina
author_facet Eckert, Karina
Britos, Paola Verónica
author_role author
author2 Britos, Paola Verónica
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Data Science Methodologies
Analytic Hierarchy Process
Personal Construction Theory
Linguistic tags
Criteria
topic Ciencias Informáticas
Data Science Methodologies
Analytic Hierarchy Process
Personal Construction Theory
Linguistic tags
Criteria
dc.description.none.fl_txt_mv The amount of data currently available for Strategic Decision Making is substantial; which is why Data Science find itself in apogee in various areas where it can be applied. Expertise respecting the areas’ methodologies is fundamental; which is why, the objective of this paper is to compare and ponder them, for which, Analytic Hierarchy Process, was utilized along with linguistic tags and Personal Construction Theory, with the purpose of establishing and prioritizing characteristics according to their degree of compliance in real validation cases. The sub-criteria were grouped in different levels, conforming a hierarchy for the present problem. The validation case consisted in determining causes for breakdowns in new automobiles as they are being transported from the factory to the concessionaires; in which the proposed model proved useful and MoProPEI could be identified as the most adequate methodology.
XVI Workshop Bases de Datos y Minería de Datos.
Red de Universidades con Carreras en Informática
description The amount of data currently available for Strategic Decision Making is substantial; which is why Data Science find itself in apogee in various areas where it can be applied. Expertise respecting the areas’ methodologies is fundamental; which is why, the objective of this paper is to compare and ponder them, for which, Analytic Hierarchy Process, was utilized along with linguistic tags and Personal Construction Theory, with the purpose of establishing and prioritizing characteristics according to their degree of compliance in real validation cases. The sub-criteria were grouped in different levels, conforming a hierarchy for the present problem. The validation case consisted in determining causes for breakdowns in new automobiles as they are being transported from the factory to the concessionaires; in which the proposed model proved useful and MoProPEI could be identified as the most adequate methodology.
publishDate 2019
dc.date.none.fl_str_mv 2019-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/91028
url http://sedici.unlp.edu.ar/handle/10915/91028
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-688-377-1
info:eu-repo/semantics/reference/hdl/10915/90359
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
477-486
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instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
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institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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