- manipulating large numbers of items
- possibly extracted from far larger datasets
Enables users to make
- decisions, or
- patterns (trend, cluster, gap, outlier...),
- groups of items, or
- individual items.
Information visualization is a complex research area. It builds on theory in information design, computer graphics, human-computer interaction and cognitive science.
Practical application of information visualization in computer programs involves selecting, transforming and representing abstract data in a form that facilitates human interaction for exploration and understanding.
Important aspects of information visualization are the interactivity and dynamics of the visual representation. Strong techniques enable the user to modify the visualization in real-time, thus affording unparalleled perception of patterns and structural relations in the abstract data in question.
Although much work in information visualization regards to visual forms, auditory and other sensory representations are also of concern.
Visualization is defined as follows [Card et al., 1998]: Visualization is the use of interactive visual representations of data to amplify cognition. This means that the data is transformed into an image, it is mapped to screen space. The image can be changed by users as they proceed working with it. This interaction is important as it allows for constant redefinition of goals when new insight into the data has been gained.
Visualization makes use of what is called external cognition [Card et al., 1998]. External resources are used for thinking. People are relieved from having to imagine everything. Instead they can just look at an image. This is only possible because human vision has a very large bandwidth, the largest of all senses [Card et al., 1998].
Information visualization is visualization of abstract data. This is data that has no inherent mapping to space. Examples for abstract data are the results of a survey or a database of the staff of a company containing names, addresses, salary and other attributes.
Information visualization should be seen in contrast to scientific visualization, which deals with physically-based data. This kind of data is defined in reference to space coordinates, which makes it relatively easy to visualize in an intuitive way. The space coordinates in the dataset are mapped to screen coordinates. Examples are geographic data and computer tomography data of a body.
Visualization of abstract data is not straightforward. One has to find a good way to map data values to screen space. It makes a difference whether the data is structured or unstructured. Examples for structured data are networks, software, and algorithms. This kind of data does not play a role in this thesis, only unstructured data is used here.
Unstructured data is a collection of records with a number of different criteria in each record. The records can be, for instance, the individual fish in a fish-catch. Of each fish the following criteria can be recorded: species, weight, sex, and different measurements of length [...]. The records are arranged in rows, the criteria make up the columns of a table. The records are also called observations. The criteria are sometimes called variables, and sometimes dimensions. [...]
Application of information visualization on the computer involves providing means to transform and represent data in a form that allows and encourages human interaction. Data can therefore be analyzed by exploration rather than pure reasoning; users can develop understanding for structures and connections in the data by observing the immediate effects their interaction has upon the visualization.
Information visualization is applied in countless areas covering every industry and all tasks where understanding of the intrinsic structure in data is crucial.
Some prominent examples are:
- Economical/financial analysis
- Representation of large hierarchies
- Medical training/assistance
- [Averbuch, 2004] Michael Averbuch, As you Like It: Tailorable Information Visualization, Database Visualization Research Group, Tufts University, 2004.
- [Card et al., 1999] Card, S. and Mackinlay, J. and Shneiderman, B., Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann Publishers, 1999.
- [Chen, 2005] Chen, C. Top 10 Unsolved Information Visualization Problems, IEEE Computer Graphics and Applications, 25(4):12-16, July-Aug. 2005.
- [Gee et al., 2005] Gee, A.G., Yu, M., and Grinstein, G.G., Dynamic and Interactive Dimensional Anchors for Spring-Based Visualizations. Technical Report, Computer Science, University of Massachussetts Lowell.
- [Keim et al., 2006] Keim, D.A.; Mansmann, F. and Schneidewind, J. and Ziegler, H., Challenges in Visual Data Analysis, Proceedings of Information Visualization (IV 2006), IEEE, p. 9-16, 2006.
- [Plaisant, 2001] Plaisant, C., Information Visualization - Lecture Notes, Created at: November 2001.
- [Tory and Möller, 2004] Melanie Tory and Torsten Möller, Human Factors in Visualization Research, IEEEE Transactions on Visualization and Computer Graphics, 10(1):72-84, January/February 2004.
- [UIUC DLI, 1998] University of Illinois at Urbana-Champaign Digital Libraries Initiative, UIUC DLI Glossary. Created: November 23, 1998. http://dli.grainger.uiuc.edu/glossary.htm
- [Usability First, 2003] Usability First, Usability Glossary. Retrieved at: 2003. http://www.usabilityfirst.com/glossary/main.cgi?function=display_term&term_id=5
- [Voigt, 2002]: Robert Voigt, An Extended Scatterplot Matrix and Case Studies in Information Visualization, Master's thesis, Hochschule Magdeburg-Stendal, 2002, Classification and Definition of Terms
- [Wikipedia, 2005] Wikipedia, Information visualization. Retrieved at: July 19, 2005. http://en.wikipedia.org/wiki/Information_visualization
- http://www.math.yorku.ca/SCS/Gallery/ has a lot of (positive and negative) examples including historical milestones.