Skip to main content

Dynamic Visual Analytics—Facing the Real-Time Challenge

  • Chapter

Abstract

Modern communication infrastructures enable more and more information to be available in real-time. While this has proven to be useful for very targeted pieces of information, the human capability to process larger quantities of mostly textual information is definitely limited. Dynamic visual analytics has the potential to circumvent this real-time information overload by combining incremental analysis algorithms and visualizations to facilitate data stream analysis and provide situational awareness. In this book chapter we will thus define dynamic visual analytics, discuss its key requirements and present a pipeline focusing on the integration of human analysts in real-time applications. To validate this pipeline, we will demonstrate its applicability in a real-time monitoring scenario of server logs.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Aggarwal, C. C. (2007). Data streams: models and algorithms. New York: Springer.

    MATH  Google Scholar 

  • Aigner, W., Miksch, S., Schumann, H., & Tominski, C. (2011). Visualization of time-oriented data. Human-computer interaction series. New York: Springer.

    Book  Google Scholar 

  • Best, D., Bohn, S., Love, D., Wynne, A., & Pike, W. (2010). Real-time visualization of network behaviors for situational awareness. Proceedings of the seventh international symposium on visualization for cyber security (pp. 79–90). New York: ACM.

    Chapter  Google Scholar 

  • Chaudhry, N., Shaw, K., & Abdelguerfi, M. (2005). Stream data management (Vol. 30). Berlin: Springer.

    Book  MATH  Google Scholar 

  • Endsley, M. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 32–64.

    Article  Google Scholar 

  • Foresti, S., Agutter, J., Livnat, Y., Moon, S., & Erbacher, R. (2006). Visual correlation of network alerts. IEEE Computer Graphics and Applications, 26, 48–59.

    Article  Google Scholar 

  • Gama, J. (2010). Knowledge discovery from data streams. Data mining and knowledge discovery series. Boca Raton: Chapman & Hall, CRC Press.

    Book  MATH  Google Scholar 

  • Golab, L., & Özsu, M. (2010). Data stream management. Morgan & Claypool Publishers.

    MATH  Google Scholar 

  • Hao, M. C., Keim, D. A., Dayal, U., Oelke, D., & Tremblay, C. (2008). Density displays for data stream monitoring. Computer Graphics Forum, 27(3), 895–902.

    Article  Google Scholar 

  • Hochheiser, H., & Shneiderman, B. (2004). Dynamic query tools for time series data sets: timebox widgets for interactive exploration. Information Visualization, 3(1), 1.

    Article  Google Scholar 

  • Kasetty, S., Stafford, C., Walker, G., Wang, X., & Keogh, E. (2008). Real-time classification of streaming sensor data. In Tools with artificial intelligence, 2008. ICTAI’08. 20th IEEE international conference (Vol. 1, pp. 149–156). IEEE.

    Chapter  Google Scholar 

  • Keim, D. A., Mansmann, F., Schneidewind, J., Thomas, J., & Ziegler, H. (2008). Visual analytics: scope and challenges. In S. Simoff, M. H. Boehlen & A. Mazeika (Eds.), Lecture notes in computer science (LNCS). Visual data mining: theory, techniques and tools for visual analytics. Berlin: Springer.

    Google Scholar 

  • Keim, D. A., Kohlhammer, J., Ellis, G., & Mansmann, F. (Eds.) (2010). Mastering the information age—solving problems with visual analytics. Eurographics.

    Google Scholar 

  • Krstajic, M., Bertini, E., Mansmann, F., & Keim, D. A. (2010). Visual analysis of news streams with article threads. In StreamKDD ’10: proceedings of the first international workshop on novel data stream pattern mining techniques (pp. 39–46). New York: ACM.

    Chapter  Google Scholar 

  • Lin, J., Keogh, E., Lonardi, S., & Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD workshop on research issues in data mining and knowledge discovery (p. 11). New York: ACM.

    Google Scholar 

  • Lin, J., Keogh, E., Lonardi, S., Lankford, J., & Nystrom, D. (2004). VizTree: a tool for visually mining and monitoring massive time series databases. In Proceedings of the thirtieth international conference on very large data bases (Vol. 30, pp. 1269–1272). VLDB Endowment.

    Google Scholar 

  • McLachlan, P., Munzner, T., Koutsofios, E., & North, S. (2008). Liverac: interactive visual exploration of system management time-series data. In Proceedings of the twenty-sixth annual SIGCHI conference on human factors in computing systems (pp. 1483–1492). New York: ACM.

    Chapter  Google Scholar 

  • Thomas, J., & Cook, K. (2005). Illuminating the path: the research and development agenda for visual analytics. Los Alamitos: IEEE Computer Society.

    Google Scholar 

  • Van Wijk, J., & Van Selow, E. (1999). Cluster and calendar based visualization of time series data. In Infovis. Los Alamitos: IEEE Computer Society.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Florian Mansmann .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London Limited

About this chapter

Cite this chapter

Mansmann, F., Fischer, F., Keim, D.A. (2012). Dynamic Visual Analytics—Facing the Real-Time Challenge. In: Dill, J., Earnshaw, R., Kasik, D., Vince, J., Wong, P. (eds) Expanding the Frontiers of Visual Analytics and Visualization. Springer, London. https://doi.org/10.1007/978-1-4471-2804-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-2804-5_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2803-8

  • Online ISBN: 978-1-4471-2804-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics