Visual Analytics: Towards Intelligent Interactive Internet and Security Solutions

  • James Davey
  • Florian Mansmann
  • Jörn Kohlhammer
  • Daniel Keim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7281)

Abstract

In the Future Internet, Big Data can not only be found in the amount of traffic, logs or alerts of the network infrastructure, but also on the content side. While the term Big Data refers to the increase in available data, this implicitly means that we must deal with problems at a larger scale and thus hints at scalability issues in the analysis of such data sets. Visual Analytics is an enabling technology, that offers new ways of extracting information from Big Data through intelligent, interactive internet and security solutions. It derives its effectiveness both from scalable analysis algorithms, that allow processing of large data sets, and from scalable visualizations. These visualizations take advantage of human background knowledge and pattern detection capabilities to find yet unknown patterns, to detect trends and to relate these findings to a holistic view on the problems. Besides discussing the origins of Visual Analytics, this paper presents concrete examples of how the two facets, content and infrastructure, of the Future Internet can benefit from Visual Analytics. In conclusion, it is the confluence of both technologies that will open up new opportunities for businesses, e-governance and the public.

Keywords

Visual Analytic Intrusion Detection System Network Infrastructure Smart City Information Visualization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Andrienko, G.L., Andrienko, N.V., Hurter, C., Rinzivillo, S., Wrobel, S.: From movement tracks through events to places: Extracting and characterizing significant places from mobility data. In: IEEE Conference on Visual Analytics Science and Technology (VAST 2011), pp. 161–170 (2011)Google Scholar
  2. 2.
    Bostock, M., Ogievetsky, V., Heer, J.: D3: Data-driven documents. IEEE Transactions on Visualization and Computer Graphics 17(12), 2301–2309 (2011)CrossRefGoogle Scholar
  3. 3.
    Card, S.K., Mackinlay, J.D., Shneiderman, B.: Readings in information visualization: using vision to think. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  4. 4.
    Cukier, K.: Data, data everywhere: A special report on managing information. The Economist 1(1), 14 (2010)MATHGoogle Scholar
  5. 5.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI Press (1996)Google Scholar
  6. 6.
    Fischer, F., Mansmann, F., Keim, D.A.: Real-Time Visual Analytics for Event Data Streams. In: Proceedings of the 2012 ACM Symposium on Applied Computing, SAC 2012. ACM (2012)Google Scholar
  7. 7.
    Fischer, F., Mansmann, F., Keim, D.A., Pietzko, S., Waldvogel, M.: Large-Scale Network Monitoring for Visual Analysis of Attacks. In: Goodall, J.R., Conti, G., Ma, K.-L. (eds.) VizSec 2008. LNCS, vol. 5210, pp. 111–118. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanovic, N., Meijers, E.: Smart cities ranking of european medium-sized cities (2009), http://www.smart-cities.eu/ (retrieved January 20, 2012)
  9. 9.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques., 3rd edn. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann Publishers Inc., Waltham (2012)Google Scholar
  10. 10.
    Johnson, B., Shneiderman, B.: Tree-maps: A space-filling approach to the visualization of hierarchical information structures. In: Proc. IEEE Conference on Visualization, pp. 284–291. IEEE (1991)Google Scholar
  11. 11.
    Keim, D.A., Kohlhammer, J., Ellis, G., Mansmann, F. (eds.): Mastering The Information Age - Solving Problems with Visual Analytics. Eurographics (2010), http://www.vismaster.eu/wp-content/uploads/2010/11/VisMaster-book-lowres.pdf
  12. 12.
    Keim, D.A., Mansmann, F., Schneidewind, J., Thomas, J., Ziegler, H.: Visual Analytics: Scope and Challenges. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds.) Visual Data Mining. LNCS, vol. 4404, pp. 76–90. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Kisilevich, S., Rohrdantz, C., Keim, D.A.: Beautiful picture of an ugly place. Exploring photo collections using opinion and sentiment analysis of user comments. In: Computational Linguistics & Applications (CLA 2010), pp. 419–428 (October 2010)Google Scholar
  14. 14.
    Krstajic, M., Najm-Araghi, M., Mansmann, F., Keim, D.: Incremental Visual Text Analytics of News Story Development. In: Proceedings of Conference on Visualization and Data Analysis, VDA 2012 (2012)Google Scholar
  15. 15.
    MacEachren, A.M., Jaiswal, A.R., Robinson, A.C., Pezanowski, S., Savelyev, A., Mitra, P., Zhang, X., Blanford, J.: Senseplace2: Geotwitter analytics support for situational awareness. In: IEEE Conference on Visual Analytics Science and Technology (VAST 2011), pp. 181–190 (2011)Google Scholar
  16. 16.
    Mansmann, F., Keim, D.A., North, S.C., Rexroad, B., Sheleheda, D.: Visual Analysis of Network Traffic for Resource Planning, Interactive Monitoring, and Interpretation of Security Threats. IEEE Transactions on Visualization and Computer Graphics 13(6) (2007)Google Scholar
  17. 17.
    Piatetsky-Shapiro, G., Frawley, W.J. (eds.): Knowledge Discovery in Databases. MIT Press (1991)Google Scholar
  18. 18.
    Rohrdantz, C., Oelke, D., Krstajic, M., Fischer, F.: Real-Time Visualization of Streaming Text Data: Tasks and Challenges. In: Workshop on Interactive Visual Text Analytics for Decision-Making at the IEEE VisWeek 2011 (2011)Google Scholar
  19. 19.
    Thomas, J.J., Cook, K.A. (eds.): Illuminating the Path: the Research and Development Agenda for Visual Analytics. IEEE CS Press (2005)Google Scholar
  20. 20.
    Viegas, F., Wattenberg, M., Van Ham, F., Kriss, J., McKeon, M.: Manyeyes: a site for visualization at internet scale. IEEE Transactions on Visualization and Computer Graphics 13(6), 1121–1128 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • James Davey
    • 1
  • Florian Mansmann
    • 2
  • Jörn Kohlhammer
    • 1
  • Daniel Keim
    • 2
  1. 1.Fraunhofer IGDGermany
  2. 2.Universität KonstanzGermany

Personalised recommendations