Monday , 16 September 2019
roen

Art. 01 – Vol. 27 – No. 2 – 2017

Big Data Visualization and Visual Analytics.
Approaches and Trends

Mihnea Horia VREJOIU
mihnea@dossv1.ici.ro
Mădălina Cornelia ZAMFIR
madalina@ici.ro
Vladimir FLORIAN
vladimir@ici.ro
National Institute for Research & Development of Informatics – ICI Bucharest

Abstract: In our days, in more and more areas, huge volumes of data – that are continuously growing – are collected and stored. The possibility of effectively using them, by extracting the useful contained information, becomes an increasing challenge. The visual analytics (VA) field tries to provide better and more effective means for humans to analyze and understand such huge data sets, thus enabling them to decide and act accordingly in real time. This paper presents an overview on what we are calling today visual analytics, with specific approaches and trends. Aspects regarding the integration of Big Data visualization techniques, data management technologies, automated methods for data mining and analytics, with human user interaction for steering the chain of processings and actions specific to visual analytics to find and extract useful information and knowledge for evaluations that may support immediate decisions and actions, are covered. The main ideas regarding the definition, method / process, and components of the visual analytics, resulted from the VisMaster Project, a Coordination Action in the FET program of EU’s FP7 for R&D, are briefly reproduced. Some of the major application areas are reviewed. Also, the current status is presented, through some open source and comercial products, and an overview of the approaches and trends in VA, with problems, challenges, opportunities and potential solutions, is summarized. Some conclusions are finally presented.

Keywords: Data Visualization, Big Data, Visual Analytics, Visual Data Mining, Data Management.

REFERENCES

  1. KEIM, D.; KOHLHAMMER, J.; ELLIS, G.; MANSMANN, F. (EDS.): Mastering The Information Age. Solving Problems with Visual Analytics. Eurographics Association, Germany, ISBN 978-3-905673-77-7, electronic version at Eurographics Digital Library: http://diglib.eg.org, 2010.
  2. AMRAII, S. A.; LEWIS, M.; SARGENT, R.; NOURBAKHSH, I.: Explorable Visual Analytics. Knowledge Discovery in Large and High–Dimensional Data. Proceedings of the Association for Computing Machinery Special Interest Group Knowledge Discovery and Data Mining (ACM SIGKDD) Workshop on Interactive Data Exploration and Analytics, New York City, USA, 2014, pp. 26-34.

View full article

  1. THOMAS, J. J.; COOK, K. A.: ILLUMINATING THE PATH: The Research and Development Agenda for Visual Analytics. IEEE Computer Society Press, Los Alamitos, 2005
  2. KEIM, D.; ANDRIENKO, G.; FEKETE, J.-D.; GORG, C.; KOHLHAMMER, J.; MELANCON, G.: Visual analytics: Definition, process, and challenges. In Information Visualization, LNCS 4950, Kerren, A. et al. (Eds.), Springer-Verlag Berlin Heidelberg, 2008, pp. 154-175.
  3. HARGER, J. R.; CROSSNO, P. J.: Comparison of open-source visual analytics toolkits. In Proceedings of the SPIE Conference on Visualization and Data Analysis, 2012.
  4. ZHANG, L.; STOFFEL, A.; BEHRISCH, M.; MITTELSTÄDT, S.; SCHRECK, T.; POMPL, R.; WEBER, S. H.; LAST, H.; KEIM, : Visual Analytics for the Big Data Era – A Comparative Review of State-of-the-Art Commercial Systems. In Proceedings of IEEE Conference on Visual Analytics Science and Technology 2012, Oct. 14-19, Seattle, WA, USA, pp. 173-182.
  5. FEKETE, J.-D.: The infovis toolkit. In INFOVIS, 2004, pp. 167-174.
  6. HEER, J. ; CARD, S. K.; LANDAY, J. A.: Prefuse: a toolkit for interactive information visualization. In Proceedings of the SIGCHI conference on Human factors in computing systems, CHI ’05, New York, NY, USA, (ACM), 2005, pp. 421-430.
  7. WEAVER, C.: Building highly-coordinated visualizations in Improvise. In INFOVIS, 2004, pp. 159-166.
  8. ***: Java Universal Network/Graph Framework, http://jung-sourceforge.net/.
  9. ***: http://www.tableausoftware.com/.
  10. ***: http://spotfire.tibco.com/.
  11. ***: http://www.qlikview.com/.
  12. ***: http://www.jmp.com/.
  13. ***: http://www.advizorsolutions.com/.
  14. ***: http://www.centrifugesystems.com/.
  15. ***: http://www.visualanalytics.com/.
  16. SCHUNN, C. D.; KLAHR, D.: A 4-space model of scientific discovery. In Proceedings of the seventeenth annual conference of the Cognitive Science Society, 1995, pp. 106-111.
  17. FRY, B. J.: Computational information design. Ph. D. Thesis, Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2004.
  18. FEKETE, J.-D.: Visual analytics infrastructures: From data management to exploration. Computer, 46(7), July 2013, pp. 22-29.
  19. HERMAN, I.; MELANCON, G.; MARSHALL, :  Graph  visualization and navigation in information visualization: A survey. IEEE Transactions on Visualization and Computer Graphics, 6(1), January 2000, pp. 24-43.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.