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Towards Big Data Interactive Visualization in Ambient Intelligence Environments

  • Giannis Drossis
  • George Margetis
  • Constantine Stephanidis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9749)

Abstract

Big Data visualization relies on an interdisciplinary research area that includes mass data storage and retrieval, operations, analytics, security, ethics as well as visualization and interaction with end users. This paper reports on the characteristics of Big Data systems, mainly focusing on information visualization and discusses a number of methods towards this direction, analyzing research issues and challenges that emerge. Additionally, this paper discusses new approaches for Big Data visualization in the context of Ambient Intelligence (AmI) environments, highlighting new aspects in the field in respect to information presentation and natural user interaction. Furthermore, a scenario of Big Data visualization in AmI environments is presented, aiming at bringing to surface the new potential of such approaches in terms of interaction simplification, and adaptation to the context of use.

Keywords

Big Data Big Data visualization Big Data interaction Ambient Intelligence Data centre infrastructure management 

Notes

Acknowledgements

The work reported in this paper has been conducted in the context of the AmI Programme of the Institute of Computer Science of the Foundation for Research and Technology-Hellas (FORTH).

References

  1. 1.
    De Mauro, A., Greco, M., Grimaldi, M.: What is big data? A consensual definition and a review of key research topics. In: AIP Conference Proceedings, vol. 1644, no. 1 (2015)Google Scholar
  2. 2.
    Ammoura, A., Zaïane, O.R., Göbel, R.: Towards a novel OLAP interface for distributed data warehouses. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 174–185. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Azri, S., Ujang, U., Castro, F.A., Rahman, A.A., Mioc, D.: Classified and clustered data constellation: an efficient approach of 3D urban data management. ISPRS J. Photogramm. Remote Sens. 113, 30–42 (2016)CrossRefGoogle Scholar
  4. 4.
    Beck, F., Burch, M., Diehl, S., Weiskopf, D.: The state of the art in visualizing dynamic graphs. EuroVis STAR (2014)Google Scholar
  5. 5.
    Berson, A., Smith, S.J.: Data Warehousing, Data Mining, and OLAP. McGraw-Hill Inc., New York (1997)Google Scholar
  6. 6.
    Bikakis, N., Liagouris, J., Krommyda, M., Papastefanatos, G.: graphVizdb: A Scalable Platform for Interactive Large Graph VisualizationGoogle Scholar
  7. 7.
    Chandramouli, B., Goldstein, J., Duan, S.: Temporal analytics on big data for web advertising. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE). IEEE (2012)Google Scholar
  8. 8.
    Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mob. Comput. 5(4), 277–298 (2009)CrossRefGoogle Scholar
  9. 9.
    Cuzzocrea, A., Mansmann, S.: OLAP visualization: models, issues, and techniques. In: Encyclopedia of Data Warehousing and Mining, pp. 1439–1446 (2009)Google Scholar
  10. 10.
    Cuzzocrea, A., Song I.-Y., Davis, K.C.: Analytics over large-scale multidimensional data: the big data revolution! In: Proceedings of the ACM 14th International Workshop on Data Warehousing and OLAP. ACM (2011)Google Scholar
  11. 11.
    Donalek, C., Djorgovski, S.G., Cioc, A., Wang, A., Zhang, J., Lawler, E., Yeh, S., Mahabal, A., Graham, M., Drake, A., Davidoff, S., Norris, J.S., Longo, G.: Immersive and collaborative data visualization using virtual reality platforms. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 609–614. IEEE, October 2014Google Scholar
  12. 12.
    Dong, X.L., Srivastava, D.: Big data integration. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE). IEEE (2013)Google Scholar
  13. 13.
    Drossis, G., Birliraki, C., Patsiouras, N., Margetis, G., Stephanidis, C.: 3-D visualization of large-scale data centres. In: Cloud Computing and Services Science. Springer International Publishing, New York (2016)Google Scholar
  14. 14.
    Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Min. Knowl. Discov. 1(1), 29–53 (1997)CrossRefGoogle Scholar
  15. 15.
    Hauser, H., Ledermann, F., Doleisch. H.: Angular brushing of extended parallel coordinates. In: IEEE Symposium on Information Visualization. INFOVIS 2002. IEEE (2002)Google Scholar
  16. 16.
    Heer, J., Shneiderman, B.: Interactive dynamics for visual analysis. Queue 10(2), 30 (2012)CrossRefGoogle Scholar
  17. 17.
    Helbig, C., Bauer, H.S., Rink, K., Wulfmeyer, V., Frank, M., Kolditz, O.: Concept and workflow for 3D visualization of atmospheric data in a virtual reality environment for analytical approaches. Environ. Earth Sci. 72(10), 3767–3780 (2014)CrossRefGoogle Scholar
  18. 18.
    Hoppenbrouwer, E., Louw, E.: Mixed-use development: theory and practice in Amsterdam’s Eastern Docklands. Eur. Plann. Stud. 13(7), 967–983 (2005)CrossRefGoogle Scholar
  19. 19.
    Inselberg, A.: The plane with parallel coordinates. Vis. Comput. 1(2), 69–91 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Lafon, S., Bouali, F., Guinot, C., Venturini, G.: On studying a 3D user interface for OLAP. Data Min. Knowl. Discov. 27(1), 4–21 (2013)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Laney, D.: 3D data management: controlling data volume, velocity and variety. META Gr. Res. Note 6, 70 (2001)Google Scholar
  22. 22.
    Li, S., Dragicevic, S., Castro, F.A., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth J., Stein A., Cheng, T.: Geospatial big data handling theory and methods: a review and research challenges. ISPRS J. Photogramm. Remote Sens. (2015)Google Scholar
  23. 23.
    Li, X., Lv, Z., Zhang, B., Wang, W., Feng, S., Hu, J.: WebVRGIS based city bigdata 3D visualization and analysis (2015). arXiv preprint arXiv:1504.01051
  24. 24.
    Mackinlay, J.D., Hanrahan, P., Stolte, C.: Show me: automatic presentation for visual analysis. IEEE Trans. Vis. Comput. Graph. 13(6), 1137–1144 (2007). http://www.tableau.com/ CrossRefGoogle Scholar
  25. 25.
    Olshannikova, E., Ometov, A., Koucheryavy, Y., Olsson, T.: Visualizing big data with augmented and virtual reality: challenges and research agenda. J. Big Data 2(1), 1–27 (2015)CrossRefGoogle Scholar
  26. 26.
    Pascal, H., Janowicz, K.: Linked data, big data, and the 4th paradigm. Semant. Web 4(3), 233–235 (2013)Google Scholar
  27. 27.
    Pienta, R., Abello, J., Kahng, M., Chau, D.H.: Scalable graph exploration and visualization: sensemaking challenges and opportunities. In: 2015 International Conference on Big Data and Smart Computing (BigComp). IEEE (2015)Google Scholar
  28. 28.
    PowerBI: powerbi.microsoft.com. Accessed 10 Mar 2016
  29. 29.
    Qlik: www.qlik.com. Accessed 10 Mar 2016
  30. 30.
    Roberts, J.C., Ritsos, P.D., Badam, S.K., Brodbeck, D., Kennedy, J., Elmqvist, N.: Visualization beyond the desktop–the next big thing. Comput. Graph. Appl. IEEE 34(6), 26–34 (2014)CrossRefGoogle Scholar
  31. 31.
    Russom, P.: Big data analytics. TDWI Best Practices Report, Fourth Quarter, pp. 1–35 (2011)Google Scholar
  32. 32.
    Schmidt, A.: Interactive context-aware systems interacting with ambient intelligence. In: Ambient Intelligence, p. 159 (2005)Google Scholar
  33. 33.
    Steed, C.A., Ricciuto, D.M., Shipman, G., Smith, B., Thornton, P.E., Wang, D., Shi, X., Williams, D.N.: Big data visual analytics for exploratory earth system simulation analysis. Comput. Geosci. 61, 71–82 (2013)CrossRefGoogle Scholar
  34. 34.
    Tukey, J.W.: Exploratory Data Analysis, pp. 2–3. Addison-Wesley, Reading (1977)zbMATHGoogle Scholar
  35. 35.
    Weber, W., Rabaey, J., Aarts, E.H.L.: Ambient Intelligence. Springer Science & Business Media, Berlin (2005)CrossRefGoogle Scholar
  36. 36.
    Wongsuphasawat, K., Moritz, D., Anand, A., Mackinlay, J.: Voyager: exploratory analysis via faceted browsing of visualization recommendations. IEEE Trans. Vis. Comput. Graph. 22(1), 649–658 (2016)CrossRefGoogle Scholar
  37. 37.
    XDAT: X-dimensional Data Analysis Tool. www.xdat.org. Accessed 10 Mar 2016
  38. 38.
    Zissis, D., Lekkas, D., Koutsabasis, P.: Design and development guidelines for real-time, geospatial mobile applications: lessons from ‘MarineTraffic’. In: Daniel, F., Papadopoulos, G.A., Thiran, P. (eds.) MobiWIS 2013. LNCS, vol. 8093, pp. 107–120. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Giannis Drossis
    • 1
  • George Margetis
    • 1
  • Constantine Stephanidis
    • 1
    • 2
  1. 1.Institute of Computer ScienceFoundation for Research and Technology - Hellas (FORTH)HeraklionGreece
  2. 2.Computer Science DepartmentUniversity of CreteHeraklionGreece

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