Projeto de remodelação do restaurante o Lagar situado em Olhão
Pina, Débora Soraia Delgado
2021
Type
article
Identifier
ALVES, Carlos Manuel de Oliveira [et al.] (2019) - MLV-Viewer: multi-level visualization in decision support systems prototype. In Iberian Conference on Information Systems and Technologies, 14, Coimbra, 19-22 de junho. [S.l.] : IEEE, p. 1-7. DOI: 10.23919/CISTI.2019.8760959
978-989-98434-9-3
10.23919/CISTI.2019.8760959
Title
MLV-Viewer: multi-level visualization in decision support systems prototype
Subject
DSSM
Multi-Level Visualization on DSS
Multi-Level Visualization on DSS
Date
2020-01-21T11:50:58Z
2020-01-21T11:50:58Z
2019-06-19
2020-01-21T11:50:58Z
2019-06-19
Description
“© © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Information visualization (InfoVis) is defined as "the use of visual, interactive and visual representations supported by computer, to increase cognition". InfoVis tools and methods help us accelerate our understanding of a large amount of data. Data visualization improves understanding, especially with multidimensional data sets. Visual analysis methods allow decision makers to combine their human flexibility, creativity and knowledge with the huge storage and processing capabilities of today's computers for information about complex problems. Using advanced visual interfaces, humans can interact directly with the data analysis, adding more to their liking and need. The MLV-Viewer prototype will be described in this article.
info:eu-repo/semantics/publishedVersion
Information visualization (InfoVis) is defined as "the use of visual, interactive and visual representations supported by computer, to increase cognition". InfoVis tools and methods help us accelerate our understanding of a large amount of data. Data visualization improves understanding, especially with multidimensional data sets. Visual analysis methods allow decision makers to combine their human flexibility, creativity and knowledge with the huge storage and processing capabilities of today's computers for information about complex problems. Using advanced visual interfaces, humans can interact directly with the data analysis, adding more to their liking and need. The MLV-Viewer prototype will be described in this article.
info:eu-repo/semantics/publishedVersion
Access restrictions
openAccess
http://creativecommons.org/licenses/by-nd/4.0/
http://creativecommons.org/licenses/by-nd/4.0/
Language
eng
Comments