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

Revista:
Icono14

ISSN: 1697-8293

Any de publicació: 2018

Títol de l'exemplar: Ibero-American data journalism: development, contestation, and social change

Volum: 16

Número: 2

Tipus: Article

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

Altres publicacions en: Icono14

Resum

Massive and open data constitute a burgeoning field of study in the current context. The evolution of technology is, in turn, increasing its degree of interactivity, configuring several scenarios of great complexity in which data is understood on the basis of our interaction with it at different levels. Technologies such as virtual reality or augmented reality present an emerging framework for visualizing, representing and understanding information. Moreover, new disciplines such as interaction design, human-computer interaction, and user experience are needed to optimally configure the representation and design of data interaction dynamics, so that they can be implemented in contexts such as education. This paper reviews the current state of interactive and immersive technology (including virtual reality and alternative reality games) and of open and massive data, to highlight potential projections and propose models of data representation based on factors such as storytelling or user experience. This paper shows the need to develop models for data use and representation in fields such as education and citizen empowerment.

Referències bibliogràfiques

  • 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