Digital Data Visualization with Interactive and Virtual Reality Tools. Review of Current State of the Art and Proposal of a Model

  1. Rubio Tamayo, Jose Luis 1
  2. Barro Hernández, Mario 23
  3. Gómez Gómez, Hernando 4
  1. 1 Universidad Rey Juan Carlos
    info

    Universidad Rey Juan Carlos

    Madrid, España

    ROR https://ror.org/01v5cv687

  2. 2 Universidad Nacional Autónoma de México
    info

    Universidad Nacional Autónoma de México

    Ciudad de México, México

    ROR https://ror.org/01tmp8f25

  3. 3 Universidad Abierta y a Distancia
    info

    Universidad Abierta y a Distancia

    Ciudad de México, México

    ROR https://ror.org/05rdf3493

  4. 4 Universidad Europea de Madrid
    info

    Universidad Europea de Madrid

    Madrid, España

    ROR https://ror.org/04dp46240

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Revista:
Icono14

ISSN: 1697-8293

Año de publicación: 2018

Título del ejemplar: Ibero-American data journalism: development, contestation, and social change

Volumen: 16

Número: 2

Tipo: Artículo

DOI: 10.7195/RI14.V16I2.1174 DIALNET GOOGLE SCHOLAR

Otras publicaciones en: Icono14

Resumen

Los datos masivos y en abierto son campos de estudio con una proyección relevante en el actual contexto. La evolución de la tecnología está, a su vez, incrementando su grado de interactividad, configurando varios escenarios de gran complejidad, en la que los datos son entendidos a partir de la interacción en diferentes niveles. Tecnologías como la realidad virtual o la realidad aumentada presentan un marco emergente para la visualización, la representación y la comprensión de la información. Por otro lado, nuevas disciplinas como el diseño de interacciones, la interacción humano-computadora o la experiencia de usuario, son necesarias para configurar de manera óptima la representación y el diseño de dinámicas interactivas con datos, de manera que sean implementados en contextos tales como la educación. El presente artículo realiza una revisión del estado de las tecnologías interactivas e inmersivas (como la realidad virtual o los juegos de realidad alternativa) y el estado de los datos masivos y/o en abierto, de manera que se puedan configurar proyecciones y proponer modelos de representación de datos a partir de factores como la narrativa o la experiencia de usuario. El artículo muestra la necesidad de desarrollar modelos en el uso y representación de datos aplicable a campos como la educación y el empoderamiento ciudadano.

Referencias bibliográficas

  • 8 Immersive Virtual Reality Visualizations. Virtual Reality Pop. Access on 2017-01-10 [https://virtualrealitypop.com/8-immersive-virtual-reality-data-visualizations-25db54de0c9#.wtffut64m]
  • Albert, W., & Tullis, T. (2013). Measuring the user experience: collecting, analyzing, and presenting usability metrics. Newnes.
  • Alyssum. Virtual Reality Interactive Data Visualization. Retrieved in 2016-12-27 from [http://www.alyssum.io/]
  • Arias-Robles, F. & García-Avilés, J.A. (2016): Definiendo la hipertextualidad. Análisis cuantitativo y cualitativo de la evolución del concepto, Icono 14, volumen 14 (2), pp. 48-68. [doi: 10.7195/ri14.v24i2.995]
  • Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., & Ives, Z. (2007). Dbpedia: A nucleus for a web of open data. In The semantic web (pp. 722-735). Springer Berlin Heidelberg. [https://doi.org/10.1007/978-3-540-76298-0_52]
  • Bertrand, F. (2017). Project NEO: Virtual Reality Data Visualization for Machine Learning. Youtube. Retrieved in 2018-04-12 from [https://www.youtube.com/watch?v=myI4P9C34A0]
  • Bowman, D. A., & McMahan, R. P. (2007). Virtual reality: how much immersion is enough?, Computer, 40(7), 36-43. [DOI: 10.1109/MC.2007.257]
  • Büttner, S., Hobohm, H. C., & Müller, L. (2011). Research data management.Cambria, E. (2016). Affective computing and sentiment analysis. IEEE Intelligent Systems, 31(2), 102-107. [DOI: 10.1109/MIS.2016.31]
  • Cooper, M., Colwell, C., & Jelfs, A. (2007). Embedding accessibility and usability: considerations for e-learning research and development projects.
  • Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331.
  • Chi, E. H. H., & Riedl, J. T. (1998, October). An operator interaction framework for visualization systems. In Information Visualization, 1998. Proceedings. IEEE Symposium (pp. 63-70). IEEE.
  • Chi, E. H. H. (2000). A taxonomy of visualization techniques using the data state reference model. In Information Visualization, 2000. InfoVis 2000. IEEE Symposium (pp. 69-75). IEEE. [DOI: 10.1109/INFVIS.2000.885092]
  • Connolly, T. M., Boyle, E. A., MacArthur, E., Hainey, T., & Boyle, J. M. (2012). A systematic literature review of empirical evidence on computer games and serious games. Computers & Education, 59(2), 661-686. [https://doi.org/10.1016/j.compedu.2012.03.004]
  • De Oliveira, M. F., & Levkowitz, H. (2003). From visual data exploration to visual data mining: a survey. IEEE Transactions on Visualization and ComputerGraphics, 9(3), 378-394. [DOI: 10.1109/TVCG.2003.1207445]
  • Djorgovski, S. G. (2012). Novel Approaches to Data Visualization Opening Remarks. MSR eScience 2012 Workshop, Chicago, Oct. 2012 [http://research.microsoft.com/en-us/um/redmond/events/escience2012/djorgovski.pdf] [http://dataverse.org/]
  • Donalek, C., Djorgovski, S. G., Cioc, A., Wang, A., Zhang, J., Lawler, E., ... & Davidoff, S. (2014, October). Immersive and collaborative data visualization using virtual reality platforms. In Big Data (Big Data), 2014 IEEE International Conference (pp. 609-614). IEEE. [https://arxiv.org/ftp/arxiv/papers/1410/1410.7670.pdf]
  • Frické, M. (2009). The knowledge pyramid: a critique of the DIKW hierarchy. Journal of information science, 35(2), 131-142.
  • Garrett, J. J. (2010). Elements of user experience, the: user-centered design for the web and beyond. Pearson Education.
  • Goldstone, R. L., Pestilli, F., & Börner, K. (2015). Self-portraits of the brain: cognitive science, data visualization, and communicating brain structure and function. Trends in cognitive sciences, 19(8), 462-474. [https://doi.org/10.1016/j.tics.2015.05.012]
  • Granić, A. (2008). Experience with usability evaluation of e-learning systems. Universal Access in the Information Society, 7(4), 209.
  • Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational technology & society, 15(3), 42–57.
  • Hall, D. L., Hall, C. M., & McMullen, S. A. (2017). 20 Perspectives on the Human Side of Data Fusion: Prospects for Improved Effectiveness Using Advanced Human–Computer Interfaces. In Handbook of multisensor data fusion: theory and practice (pp. 537-548). CRC Press.
  • Hassan Montero, Y. (2002). Introducción a la Usabilidad. No sólo usabilidad, (1).
  • Heer, J., Card, S. K., & Landay, J. A. (2005, April). Prefuse: a toolkit for interactive information visualization. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 421-430). ACM. Retrieved on 2017-02-25 from [http://vis.stanford.edu/files/2005-prefuse-CHI.pdf]
  • Helbig, C., Bauer, H. S., Rink, K., Wulfmeyer, V., Frank, M., & Kolditz, O. (2014). Concept and workflow for 3D visualization of atmospheric data in a virtual reality environment for analytical approaches. Environmental Earth Sciences, 72(10), 3767-3780. [https://doi.org/10.1007/s12665-014-3136-6]
  • Huang, B., Jiang, B., & Li, H. (2001). An integration of GIS, virtual reality and the Internet for visualization, analysis and exploration of spatial data. International Journal of Geographical Information Science, 15(5), 439-456. [https://doi.org/10.1080/13658810110046574]
  • iViz. Access on 2016-12-20 [http://www.forbes.com/sites/bernardmarr/2016/05/04/how-vr-will-revolutionize-big-data-visualizations/#463379234ac5] [http://anwell.me/articles/iviz/]
  • Jennex, M. E. (2009, January). Re-visiting the knowledge pyramid. In System Sciences, 2009. HICSS’09. 42nd Hawaii International Conference on (pp. 1-7). IEEE. [DOI: 10.1109/HICSS.2009.361]
  • Katsanos, C., Tselios, N., & Xenos, M. (2012, October). Perceived usability evaluation of learning management systems: a first step towards standardization of the System Usability Scale in Greek. In Informatics (PCI), 2012 16th Panhellenic Conference on (pp. 302-307). IEEE. [DOI: 10.1109/PCi.2012.38]
  • Keele, S. (2007). Guidelines for performing systematic literature reviews in software engineering. In Technical report, Ver. 2.3 EBSE Technical Report. EBSE. sn. [doi:10.1145/1134285.1134500]
  • Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering–a systematic literature review. Information and software technology, 51(1), 7-15. [https://doi.org/10.1016/j.infsof.2008.09.009]
  • Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.
  • Kosmadoudi, Z., Lim, T., Ritchie, J., Louchart, S., Liu, Y., & Sung, R. (2013). Engineering design using game-enhanced CAD: The potential to augment the user experience with game elements. Computer-Aided Design, 45(3), 777-795. [https://doi.org/10.1016/j.cad.2012.08.001]
  • Kukulska-Hulme, A. & Shield, L. (2004). Usability and pedagogical design: Are language learning websites special? In ED-MEDIA 2004 (pp. 4235-4242).
  • Laha, B. & Bowman, D. A. (2012). Identifying the benefits of immersion in virtual reality for volume data visualization. In Immersive visualization revisited workshop of the IEEE VR conference (pp. 1-2).
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. [doi:10.1038/nature14539]
  • Marr, B. (2016). How VR will Revolutionize Big Data Visualization. Forbes On Line (May, 4, 2016). Access on 2017-02-12 from [https://www.forbes.com/sites/bernardmarr/2016/05/04/how-vr-will-revolutionize-big-data-visualizations/#c4a2845e1512]
  • Mattingly, K. D., Rice, M. C., & Berge, Z. L. (2012). Learning analytics as a tool for closing the assessment loop in higher education. Knowledge Management & E-Learning: An International Journal, 4(3), 236-247.
  • McCandless, D. (2012). Information Is Beautiful. Collins. New edition (December 6, 2012)
  • McCandless, D. (2014). Knowledge Is Beautiful: Impossible Ideas, Invisible Patterns, Hidden Connections. Harper Design (October 21, 2014)
  • Murray-Rust, P., Neylon, C., Pollock, R., & Wilbanks, J. (2010). Panton Principles, Principles for open data in science. Panton Principles.
  • Nirvaniq Labs. Retrieved 2017-03-10 from [https://nirvaniq.com/] Prefuse. Retrieved 2017-03-10 from: [http://prefuse.org/doc/manual/introduction/structure/].
  • Nogueira, P. A., Torres, V., & Rodrigues, R. (2013). Automatic emotional reactions identication: a software tool for offline user experience research. In Entertainment computing–ICEC 2013 (pp. 164-167). Springer Berlin Heidelberg [https://doi.org/10.1007/978-3-642-41106-9_22]
  • Patterson, R. E., Blaha, L. M., Grinstein, G. G., Liggett, K. K., Kaveney, D. E., Sheldon, K. C., ... & Moore, J. A. (2014). A human cognition framework for information visualization. Computers & Graphics, 42, 42-58. [https://doi.org/10.1016/j.cag.2014.03.002]
  • Pavlik, J. V. (2015). Transformation: examining the implications of emerging technology for journalism, media and society. Athens J Mass Media Commun, 1(1), 9-24.
  • Peled, A. (2011). When transparency and collaboration collide: The USA open data program. Journal of the American society for information science and technology, 62 (11), 2085-2094. [https://doi.org/10.1002/asi.21622]
  • Picard, R. W. (1995). Affective computing.
  • Raya, L., & Sánchez, A. (2014). Análisis y Visualización de Big Data mediante técnicas de realidad virtual. Big Data Visual Analytics using virtual reality techniques. ArDIn. Arte, Diseño e Ingeniería (3).
  • Rossi, R., & Ahmed, N. (2015, January). The Network Data Repository with Interactive Graph Analytics and Visualization. In AAAI (Vol. 15, pp. 4292-4293).
  • Rubio-Tamayo, J. L. & Gértrudix Barrio, M. (2016): Realidad Virtual (HMD) e Interacción desde la Perspectiva de la Construcción Narrativa y la Comunicación: Propuesta Taxonómica, Icono 14, volumen 14 (2), pp. 1-24. doi: 10.7195/ri14.v24i2.965
  • Ryan, M. L. (2015). Narrative as Virtual Reality 2: Revisiting Immersion and Interactivity in Literature and Electronic Media. JHU Press.
  • Satyanarayan, A., & Heer, J. (2014, June). Lyra: An interactive visualization design environment. In Computer Graphics Forum (Vol. 33, No. 3, pp. 351-360). [https://doi.org/10.1111/cgf.12391]
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. [https://doi.org/10.1016/j.neunet.2014.09.003]
  • Schwendimann, B., Rodriguez-Triana, M., Vozniuk, A., Prieto, L., Boroujeni, M., Holzer, A., ... & Dillenbourg, P. (2016). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies. DOI: 10.1109/TLT.2016.2599522
  • Seminario Internacional Escenarios 2020, Analíticas del aprendizaje, hacia una personalización de las ayudas educativas [video completo], Access on december 2016, [https://www.youtube.com/watch?v=LcEwMuze7cM].
  • Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30.
  • Song, W., Wu, D., Wong, R., Fong, S., & Cho, K. (2015, October). A real-time interactive data mining and visualization system using parallel computing. In Digital Information Management (ICDIM), 2015 Tenth International Conference on (pp. 10-13). IEEE. DOI: 10.1109/ICDIM.2015.7381890
  • Telea, A. C. (2014). Data visualization: principles and practice. CRC Press.
  • Tramullas, J. (2003). Documentos y servicios digitales: de la usabilidad al diseño centrado en el usuario. El profesional de la información, 12(2), 107-110.
  • Underwood, J. (2016). Immersive Data Visualization with Virtual Reality. Access on 2016-05-05. [http://www.jenunderwood.com/2016/05/05/immersive-data-visualization-virtual-reality/]
  • Wexelblat, A. (Ed.). (2014). Virtual reality: applications and explorations. Academic Press.
  • Wong, S. K. B., Nguyen, T. T., Chang, E., & Jayaratna, N. (2003, November). Usability metrics for e-learning. In OTM Confederated International Conferences” On the Move to Meaningful Internet Systems (pp. 235-252). Springer Berlin Heidelberg. http://link.springer.com/chapter/10.1007/978-3-540-39962-9_34
Fuente de los datos: Dialnet