@conference{umaquinga2020,

title = {Interactive Visualization Interfaces for Big Data Analysis Using Combination of Dimensionality Reduction Methods: A Brief Review},

author = {Ana C. Umaquinga-Criollo and Diego H. Peluffo-Ordóñez and Paúl D. Rosero-Montalvo and Pamela E. Godoy-Trujillo and Henry Benítez-Pereira},

editor = {Springer Charm },

url = {https://link.springer.com/chapter/10.1007/978-3-030-37221-7_17

https://doi.org/10.1007/978-3-030-37221-7_17},

doi = {https://doi.org/10.1007/978-3-030-37221-7_17},

isbn = {978-3-030-37220-0},

year  = {2020},

date = {2020-01-03},

booktitle = {Technology, Sustainability and Educational Innovation (TSIE)},

pages = {193-203},

publisher = {Springer, Cham},

abstract = {The Big Data analysis allows to generate knowledge based on mathematical models that surpass human capabilities, and therefore it is necessary to have robust computer systems. In this connection, the dimensionality reduction (DR) allows to perform approximations to make data perceptible in a simple and compact way while also the computational cost is reduced. Additionally, interactive interfaces enable the user to work with algorithms involving complex mathematical and statistical processes typically aimed at providing weighting factors to each RD algorithm to find the best way to represent data at a low dimension. In this study, a bibliographic re-view of the different models of interactive interfaces for the analysis of Big Data using RD is presented, by considering different, existing proposals and approaches on how to display the information. Particularly, those approaches based on mental processes and uses of color along with an intuitive handling are of special interest.},

note = { Print ISBN 978-3-030-37220-0 Online ISBN 978-3-030-37221-7 },

keywords = {Big data, Business intelligence, data mining, Dimensionality reduction, Interactive interface},

pubstate = {published},

tppubtype = {conference}

}

@conference{Anaya2018,

title = {Morfologías visuales de representación de datos como aumento de capacidades analíticas humanas: Una revisión de literatura},

author = {Andrés Javier Anaya Isaza and Ana Cristina Umaquinga Criollo and Gabriela Narváez Olmedo and Paúl David Rosero Montalvo and Diego Hernán Peluffo Ordóñez},

url = {https://www.researchgate.net/publication/322509217_Morfologias_visuales_de_representacion_de_datos_como_aumento_de_capacidades_analiticas_humanas_Una_revision_de_literatura 

https://issuu.com/utnuniversity/docs/ebook_aportes_de_la_ingenieria_2017/374},

isbn = {978-9942-984-97-5},

year  = {2017},

date = {2017-11-20},

booktitle = {Aportes de la Ingeniería para el desarrollo regional 

I Congreso Internacional de Ingenierías 2017},

pages = {375-381},

publisher = {UTN},

abstract = {La representación visual de datos es una técnica de extracción de conocimiento que permite tener una percepción de toda la información disponible dentro de Big Data. Para lograr el objetivo de captación de la atención humana, es necesario representar el conjunto de datos de una manera intuitiva. De esta forma, el usuario pueda tomar decisiones adecuadas. Consecuentemente, el mejoramiento de la experiencia humano computador se basa en el uso de técnicas de análisis de datos, donde los recursos computacionales deben ser optimizados. En este trabajo, se desarrolla una metodología con una investigación de tipo descriptivo, exploratorio y documental con los diferentes enfoques de visualización de datos orientados al análisis exploratorio para el descubrimiento científico y aumento de las capacidades humanas como apoyo a las decisiones automáticas. 

 

Abstract 

 

Visual representation is an approach to extract knowledge, which enables users to perceive the information whitin a context of Big Data. To involve the human perception into the data analysis, an inuitive data representation is needed. Consequently, any user will be able to make more adequate decisions. Indeed, the enhancement of human-computer interaction is based on the use of data anaylisis techniques while computational broad is optimized. In this work, a descriptive, exploratory and documental methodology is presented aimed at highlighting the benefit of involving the human skills within the process of automatic decision making.},

keywords = {Big data, data mining, minería de datos, morfología visual, visual morphology, visualización, visualization},

pubstate = {published},

tppubtype = {conference}

}