VALS: A Visual Analytics Framework for Longitudinal Studies

El análisis visual de datos ayuda a comprender distintos tipos de fenómenos al permitir a los expertos explorar en busca de relaciones, patrones, valores atípicos, cambios inesperados y mucho más. Los expertos necesitan herramientas que les ayuden a encontrar información útil y procesable en los dat...

Full description

Autores:
Gómez Betancur, Duván Alberto
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2022
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/66426
Acceso en línea:
http://hdl.handle.net/1992/66426
Palabra clave:
Estudios de cohortes
Análisis exploratorio de datos
Datos longitudinales
Análisis visual
Visualización de información
Ingeniería
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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oai_identifier_str oai:repositorio.uniandes.edu.co:1992/66426
network_acronym_str UNIANDES2
network_name_str Séneca: repositorio Uniandes
repository_id_str
dc.title.none.fl_str_mv VALS: A Visual Analytics Framework for Longitudinal Studies
title VALS: A Visual Analytics Framework for Longitudinal Studies
spellingShingle VALS: A Visual Analytics Framework for Longitudinal Studies
Estudios de cohortes
Análisis exploratorio de datos
Datos longitudinales
Análisis visual
Visualización de información
Ingeniería
title_short VALS: A Visual Analytics Framework for Longitudinal Studies
title_full VALS: A Visual Analytics Framework for Longitudinal Studies
title_fullStr VALS: A Visual Analytics Framework for Longitudinal Studies
title_full_unstemmed VALS: A Visual Analytics Framework for Longitudinal Studies
title_sort VALS: A Visual Analytics Framework for Longitudinal Studies
dc.creator.fl_str_mv Gómez Betancur, Duván Alberto
dc.contributor.advisor.none.fl_str_mv Charpak, Nathalie
Hernández Peñaloza, José Tiberio
dc.contributor.author.none.fl_str_mv Gómez Betancur, Duván Alberto
dc.contributor.jury.none.fl_str_mv Hagen, Hans
Branch, John William
Ahumada, César
Villamil Giraldo, María del Pilar
dc.contributor.researchgroup.es_CO.fl_str_mv Imagine: Computación Visual, I+D+I
dc.subject.keyword.none.fl_str_mv Estudios de cohortes
Análisis exploratorio de datos
Datos longitudinales
Análisis visual
Visualización de información
topic Estudios de cohortes
Análisis exploratorio de datos
Datos longitudinales
Análisis visual
Visualización de información
Ingeniería
dc.subject.themes.es_CO.fl_str_mv Ingeniería
description El análisis visual de datos ayuda a comprender distintos tipos de fenómenos al permitir a los expertos explorar en busca de relaciones, patrones, valores atípicos, cambios inesperados y mucho más. Los expertos necesitan herramientas que les ayuden a encontrar información útil y procesable en los datos para poder comprobar sus hipótesis y desarrollar otras nuevas. Esta necesidad se hace más evidente en los estudios longitudinales, en los que suele haber un gran número de variables y el proceso que se analiza también puede ser complejo. Presentamos VALS (Visual Analytics in Longitudinal Studies), un framework para explorar visualmente datos de estudios longitudinales. VALS incluye un modelo de datos, un modelo de categorización de tareas y un enfoque hacia la orientación de los usuarios mediante técnicas de ingeniería de características y visualizaciones interactivas, todo lo cual ayuda a los analistas a realizar sus tareas de análisis. La construcción de VALS estuvo acompañada por expertos en estudios clínicos longitudinales. También hemos desarrollado un prototipo de herramienta para un estudio de caso utilizando conjuntos de datos del mundo real. Las pruebas recogidas en el estudio de caso demuestran la utilidad de una herramienta de análisis visual basada en VALS.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022-07-19
dc.date.accessioned.none.fl_str_mv 2023-04-26T15:57:27Z
dc.date.available.none.fl_str_mv 2023-04-26T15:57:27Z
dc.type.es_CO.fl_str_mv Trabajo de grado - Doctorado
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.content.es_CO.fl_str_mv Text
dc.type.redcol.none.fl_str_mv https://purl.org/redcol/resource_type/TD
format http://purl.org/coar/resource_type/c_db06
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dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/1992/66426
dc.identifier.doi.none.fl_str_mv 10.57784/1992/66426
dc.identifier.instname.es_CO.fl_str_mv instname:Universidad de los Andes
dc.identifier.reponame.es_CO.fl_str_mv reponame:Repositorio Institucional Séneca
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url http://hdl.handle.net/1992/66426
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dc.language.iso.es_CO.fl_str_mv eng
language eng
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spelling Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttps://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Charpak, Nathalie3bc5ae8c-1bf4-4b3d-a96a-5a122bb76ad4600Hernández Peñaloza, José Tiberiovirtual::4212-1Gómez Betancur, Duván Alberto9c633435-0066-47a3-8893-7bbb17f5f9a0600Hagen, HansBranch, John WilliamAhumada, CésarVillamil Giraldo, María del PilarImagine: Computación Visual, I+D+I2023-04-26T15:57:27Z2023-04-26T15:57:27Z2022-07-19http://hdl.handle.net/1992/6642610.57784/1992/66426instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/El análisis visual de datos ayuda a comprender distintos tipos de fenómenos al permitir a los expertos explorar en busca de relaciones, patrones, valores atípicos, cambios inesperados y mucho más. Los expertos necesitan herramientas que les ayuden a encontrar información útil y procesable en los datos para poder comprobar sus hipótesis y desarrollar otras nuevas. Esta necesidad se hace más evidente en los estudios longitudinales, en los que suele haber un gran número de variables y el proceso que se analiza también puede ser complejo. Presentamos VALS (Visual Analytics in Longitudinal Studies), un framework para explorar visualmente datos de estudios longitudinales. VALS incluye un modelo de datos, un modelo de categorización de tareas y un enfoque hacia la orientación de los usuarios mediante técnicas de ingeniería de características y visualizaciones interactivas, todo lo cual ayuda a los analistas a realizar sus tareas de análisis. La construcción de VALS estuvo acompañada por expertos en estudios clínicos longitudinales. También hemos desarrollado un prototipo de herramienta para un estudio de caso utilizando conjuntos de datos del mundo real. Las pruebas recogidas en el estudio de caso demuestran la utilidad de una herramienta de análisis visual basada en VALS.Doctor en IngenieríaDoctoradoAnalítica Visual y Visualización de Información122application/pdfengUniversidad de los AndesDoctorado en IngenieríaFacultad de IngenieríaDepartamento de Ingeniería Sistemas y ComputaciónVALS: A Visual Analytics Framework for Longitudinal StudiesTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttps://purl.org/redcol/resource_type/TDEstudios de cohortesAnálisis exploratorio de datosDatos longitudinalesAnálisis visualVisualización de informaciónIngenieríaM Ali et al. "Clustering and Classification for Time Series Data in Visual Analytics: A Survey". In: ieeexplore.ieee.org (2019).Diego A. Angulo et al. "A multi-facetted visual analytics tool for exploratory analysis of human brain and function datasets". 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