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Goal-Based Selection of Visual Representations for Big Data Analytics

  • Matteo Golfarelli
  • Tommaso Pirini
  • Stefano Rizzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10651)

Abstract

The H2020 TOREADOR Project adopts a model-driven architecture to streamline big data analytics and make it widely available to companies as a service. Our work in this context focuses on visualization, in particular on how to automate the translation of the visualization objectives declared by the user into a suitable visualization type. To this end we first define a visualization context based on seven prioritizable coordinates for assessing the user’s objectives and describing the data to be visualized; then we propose a skyline-based technique for automatically translating a visualization context into a set of suitable visualization types. Finally, we evaluate our approach on a real use case excerpted from the pilot applications of TOREADOR.

Keywords

Big data Visual analytics Skyline queries 

References

  1. 1.
    Abela, A.: Advanced Presentations by Design. Pfeiffer, San Francisco (2008)Google Scholar
  2. 2.
    Ardagna, C., Bellandi, V., Damiani, E., Bezzi, M., Hebert, C.: A model-driven methodology for big data analytics-as-a-service. In: Proceedings of the IEEE International Congress on Big Data, Honolulu, Hawaii (2017)Google Scholar
  3. 3.
    Bertin, J.: Semiology of Graphics. Esri Press, Redlands (1983)Google Scholar
  4. 4.
    Börner, K.: Atlas of Knowledge: Anyone Can Map. MIT Press, Cambridge (2015)Google Scholar
  5. 5.
    Chandra, J., Madhu Shudan, S.: IBA graph selector algorithm for big data visualization using defense data set. Int. J. Sci. Eng. Res. 4(3), 1–7 (2013)Google Scholar
  6. 6.
    Dadzie, A.S., Rowe, M.: Approaches to visualising linked data: a survey. Semant. web 2(2), 89–124 (2011)Google Scholar
  7. 7.
    Few, S.: Show Me The Numbers: Designing Tables and Graphs to Enlighten. Analytics Press, Berkeley (2004)Google Scholar
  8. 8.
    Kano, N., Nobuhiku, S., Fumio, T., Shinichi, T.: Attractive quality and must-be quality. J. Jpn. Soc. Qual. Control 14(2), 39–48 (1984)Google Scholar
  9. 9.
    Keim, D.: Exploring big data using visual analytics. In: Proceedings of the EDBT/ICDT Workshops (2014)Google Scholar
  10. 10.
    Keim, D.A.: Information visualization and visual data mining. IEEE Trans. Vis. Comput. Graph. 8(1), 1–8 (2002)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Kleppe, A., Warmer, J., Bast, W.: MDA Explained - The Model Driven Architecture: Practice and Promise. Addison-Wesley, Boston (2003)Google Scholar
  12. 12.
    Marty, R.: Applied Security Visualization. Addison-Wesley, Boston (2009)Google Scholar
  13. 13.
    Mindolin, D., Chomicki, J.: Preference elicitation in prioritized skyline queries. VLDB J. 20(2), 157–182 (2011)CrossRefzbMATHGoogle Scholar
  14. 14.
    Peña, O., Aguilera, U., López-de-Ipiña, D.: Exploring LOD through metadata extraction and data-driven visualizations. Program 50(3), 270–287 (2016)CrossRefGoogle Scholar
  15. 15.
    Russom, P.: Big data analytics. Technical report, TDWI Best Practices Report (2011)Google Scholar
  16. 16.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of the IEEE Symposium on Visual Languages, pp. 336–343 (1996)Google Scholar
  17. 17.
    Stevens, S.S.: On the theory of scales of measurement. Science 103(2684), 677–680 (1946)CrossRefzbMATHGoogle Scholar
  18. 18.
    Wehrend, S., Lewis, C.: A problem-oriented classification of visualization techniques. In: Proceedings of the IEEE Conference on Visualization, pp. 139–143 (1990)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Matteo Golfarelli
    • 1
    • 2
  • Tommaso Pirini
    • 1
  • Stefano Rizzi
    • 1
    • 2
  1. 1.DISIUniversity of BolognaBolognaItaly
  2. 2.CINIRomaItaly

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