Methodology for the comprehensive application of visualization techniques in data science as support for decision making
Abstract
The advancement of technology favors the generation and increase of large volumes of information that exceed the cognitive and perceptual capacities of the people, creating the need to find new ways to represent this information under new schemes that summarize and present it in a more understandable and friendly way. The present research seeks to make an assessment of the attributes of certain visualization techniques in order to obtain a hierarchy of the efficiency of them and at the same time, it tries to identify problems in the visualization of the data offering to analysts and / or borrowers of decisions, a frame of reference that helps them in the selection of the visualization tool that best suits their needs.
Full Text:
PDF (Spanish)References
Gorodov, E. Y. E., y Gubarev, V. V. E. (2013). “Analytical review of data visualization methods in application to big data”. Journal of Electrical and Computer Engineering, 2013(22). DOI: 10.1155/2013/969458. Recuperado de: http://dl.acm.org/citation.cfm?id=2610958
Stacey, M., Salvatore, J., & Jorgensen, A. (2013). Visual Intelligence: Microsoft Tools and Techniques for Visualizing Data. John Wiley & Sons.
Wang, L., Wang, G., & Alexander, C. A. (2015). Big data and visualization: methods, challenges and technology progress. Digital Technologies, 1(1), 33-38.
Tascón, M. (2013). Introducción: “Big Data. Pasado, presente y future”. Telos: Cuadernos de comunicación e innovación, (95), 47-50.
Casner, S.M. (1991). “A task-analytic approach to the automated design of graphic presentation”. ACM Transactions on Graphics, 10(2), 111–151. Recuperado de: http://dl.acm.org/citation.cfm?id=108361
Bertin, J. (1983). Semiology of Graphics: Diagrams, networks, maps. Estados Unidos de Norteamérica: University of Wisconsin Press
Nowell, L., Schulman, R. y Hix, D. (2002). “Graphical Encoding for Information Visualization: An Empirical Study”. Proceedings of the IEEE Symposium on Information Visualization, DOI: 10.1109/INFVIS.2002.1173146. Recuperado de: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1173146
Guzmán, O. (2007). “Taxonomías de la visualización de la información”. Sistemas y Telemática, 5(10), 87-125. Recuperado de: https://www.icesi.edu.co/revistas/index.php/sistemas_telematica/article/viewFile/984/1009
Hurtado, L., Catrillón, O. y Olivar, G. (2013). “Una Metodología para la Evaluación de Usabilidad de Interfaces de Supervisión Industrial”. Información Tecnológica, 24(4), 95-104. Recuperado de: http://dx.doi.org/10.4067/S0718-07642013000400011
Gómez, A., García, F. y Therón, R. (2014). “Analítica Visual en E-Learning”. El Profesionista de la Información, 23(3), 236-245. Recuperado de: http://www.elprofesionaldelainformacion.com/contenidos/2014/mayo/03.pdf
Cleveland, W. y McGill, R. (1984). “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods”. Journal of the American Statistical Association, 79(387), 531–554. DOI: 10.1080/01621459.1984.10478080. Recuperado de: http://www.tandfonline.com/doi/abs/10.1080/01621459.1984.10478080
Cox, R. (1999). “Representation construction, externalised cognition and individual differences”. Learning and Instruction, 9(4), 343–363. DOI: 10.1016/S0959-4752(98)00051-6. Recuperado de: http://www.sciencedirect.com/science/article/pii/S0959475298000516
Zhu, Y., Suo, X. y Owen, G.S. (2007). “Complexity Analysis for Information Visualization Design and Evaluation”. Advances in Visual Computing, 4841, Heidelberg, Berlín, Alemania: Springer-Verlag.
Rossi, R., y Hirama, K. (2015). “Characterizing Big Data Management”. Issues in Informing Science and Information Technology, 12, 165-180. Recuperado de: http://iisit.org/Vol12/IISITv12p165-180Rossi1921.pdf
Farooq, M., Waseem, M., Mazhar, S., Khairi, A., y Kamal, T. (2015). “A Review on Internet of Things (IoT)”. International Journal of Computer Applications, 113(1), 1-7. Recuperado de: http://www.researchgate.net/profile/Muhammad_Farooq75/publication/273693976_A_Review_on_Internet_of_Things_(IoT)/links/5508ac290cf26ff55f83af53.pdf
Tene, O., y Polonetsky, J. (2013). “Big Data for all: Privacy and user control in the age of analytics”. Northwestern Journal of Technology and Intellectual Property, 11(5). Recuperado de: http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1/
Fuschi, D. L., y Tvaronavičienė, M. (2011). “Sustainable development, Big Data and supervisory control: service quality in banking sector”. Journal of Security and Sustainability Issues, 3(3), 5-14. Recuperado de: http://dx.doi.org/10.9770/jssi.2014.3.3(1)
Villars, R.L., Olofson C.W. y Eastwood M. (2011). “White Paper, Big Data: What It Is and Why You Should Care”. IDC Analyze the Future. 1-14. Recuperado: http://www.admin-magazine.com/HPC/content/download/5604/49345/file/IDC_Big%20Data_whitepaper_final.pdf
Kirk, A. (2012). “Data Visualization: a successful design process. Packt Publishing Ltd. Choy, J. Visualizing Data Techniques, Including Auto-Charting and Big Data for SAS”. Global Forum 2012. SAS Institute Inc., Cary, NC, USA.
Hatle, D. G. N. G. S., Sayeed, A., Gupta, D., Neela, G. P., & Winter, S. (2013). “Pentaho data integration tool”. Business Intelligence Tool, 2-18.
Flare data visualization for the web, recuperado en Diciembre 18, 2016, de: http://flare.prefuse.org/
Viegas, F. B., Wattenberg, M., Van Ham, F., Kriss, J., & McKeon, M. (2007). “Manyeyes: a site for visualization at internet scale”. IEEE transactions on visualization and computer graphics, 13(6), 1121-1128.
Heer, J., Mackinlay, J., Stolte, C., & Agrawala, M. (2008). “Graphical histories for visualization: Supporting analysis, communication, and evaluation”. IEEE transactions on visualization and computer graphics, 14(6), 1189-1196.
Chittaro, L., & Combi, C. (2001). “Representation of temporal intervals and relations: Information visualization aspects and their evaluation”. In Temporal Representation and Reasoning, 2001. TIME 2001. Proceedings. Eighth International Symposium on (pp. 13-20). IEEE.
Wiss, U., & Carr, D. (1998). A cognitive classification framework for 3-dimensional information visualization. Luleå tekniska universitet.
Daassi, C., Nigay, L., & Fauvet, M. C. (2005). “A taxonomy of temporal data visualization techniques”. Information-Interaction-Intelligence, 5(2), 41-63.
Wohlfart, E., Aigner, W., Bertone, A., & Miksch, S. (2008, July). “Comparing information visualization tools focusing on the temporal dimensions”. In 2008 12th International Conference Information Visualisation (pp. 69-74). IEEE.
Sun, G. D., Wu, Y. C., Liang, R. H., & Liu, S. X. (2013). “A survey of visual analytics techniques and applications: State-of-the-art research and future challenges”. Journal of Computer Science and Technology, 28(5), 852-867.
Freedman, E. y Shah, P. (2002). “Toward a Model of Knowledge-Based Graph Comprehension”. Diagrammatic Representation and Inference. DOI: 10.1007/3-540-46037-3_3. Recuperado de: http://link.springer.com/chapter/10.1007/3-540-46037-3_3.
Refbacks
- There are currently no refbacks.
