Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Visual Analytics

  • Daniel A. KeimEmail author
  • Florian Mansmann
  • Andreas Stoffel
  • Hartmut Ziegler
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1122


Visual analysis; Visual data analysis; Visual data mining


Visual analytics is the science of analytical reasoning supported by interactive visual interfaces. Over the last decades, data was produced at an incredible rate. However, the ability to collect and store this data is increasing at a faster rate than the ability to analyze it. While purely automatic or purely visual analysis methods were developed in the last decades, the complex nature of many problems makes it indispensable to include humans at an early stage in the data analysis process. Visual analytics methods allow decision makers to combine their flexibility, creativity, and background knowledge with the enormous storage and processing capacities of today’s computers to gain insight into complex problems. The goal of visual analytics research is thus to turn the information overload into an opportunity: Decision-makers should be enabled to examine this massive, multi-dimensional, multi-source,...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Card SK, Mackinlay JD, Shneiderman B, editors. Readings in information visualization: using vision to think. San Francisco: Morgan Kaufmann; 1999.Google Scholar
  2. 2.
    Chen C. Information visualization – beyond the horizon. 2nd ed. Berlin: Springer; 2004.Google Scholar
  3. 3.
    Keim DA. Visual exploration of large data sets. Commun ACM. 2001;44(8):38–44.CrossRefGoogle Scholar
  4. 4.
    Keim DA, Thomas J. Scope and challenges of visual analytics. Tutorial at IEEE Visualization Conference; 2007.Google Scholar
  5. 5.
    Spence R. Information visualization – design for interaction. 2nd ed. Harlow: Pearson Education Limited; 2006.Google Scholar
  6. 6.
    Thomas J, Cook K. Illuminating the path: research and development agenda for visual analytics. Los Alamitos: IEEE-Press; 2005.Google Scholar
  7. 7.
    Tukey JW. Exploratory data analysis. Reading: Addison-Wesley; 1977.zbMATHGoogle Scholar
  8. 8.
    Ware C. Information visualization – perception for design. 1st ed. San Francisco: Morgan Kaufmann; 2000.Google Scholar
  9. 9.
    Wong PC, Thomas J. Visual analytics – guest editors’ introduction. IEEE Trans Comput Graph Appl. 2004;24(5):20–1.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Daniel A. Keim
    • 1
    Email author
  • Florian Mansmann
    • 2
  • Andreas Stoffel
    • 2
  • Hartmut Ziegler
    • 2
  1. 1.Computer Science DepartmentUniversity of KonstanzKonstanzGermany
  2. 2.University of KonstanzKonstanzGermany

Section editors and affiliations

  • Daniel A. Keim
    • 1
  1. 1.Computer Science DepartmentUniversity of KonstanzKonstanzGermany