Database Reference
In-Depth Information
5
Conclusion
The three discussions of Information Visualization presented here draw on existing
theories of data-centric prediction, information communication and scientific model-
ing, and relate in different ways to the linguistic framework defined in the introduc-
tion. A single uniting theory of Information Visualization may be impossible due to
its strong relationship to and use of several other diverse disciplines (e.g. psychology
(perception, cognition and learning), graphic design and aesthetics).
Investigating theoretical approaches used in other disciplines, and their relation to
Information Visualization, is an obvious way forward, and can provide a useful way
for researchers in the area to present, discuss and validate their ideas; it is hoped that
the over-arching linguistics-based framework of representation, user exploration and
manipulation, and system exploration and manipulation will prove useful in linking
the constituent theories together. The more solid theoretical analyses that Information
Visualization researchers or tool designers can call on in defending or validating their
work, the more secure the discipline will be.
References
1. de Saussure, F.: Writings in General Linguistics. In: Bouquet, S., Engler, R., Sanders, C.,
Pires, M. (eds.), Oxford University Press, Oxford (2006)
2. Bakhtin, M.: The Dialogic Imagination, University of Texas Press (1981), quoted in Ball,
A.F., Freedman, S.W.: Bhaktinian Persepectives on Language, Literacy, and Learning,
Cambridge University Press, Cambridge (2004)
3. Andrienko, N., Andrienko, G.: Exploratory Analysis of Spatial and Temporal Data: A Sys-
tematic Approach. Springer, Heidelberg (2006)
4. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in
databases. AI Magazine 17, 37-54 (1996)
5. Card, S.K., Moran, T.P., Newell, A.: The Psychology of Human-Computer Interaction.
Erlbaum Associates, Hillsdale (1983)
6. Schneider, T.D.: Information Theory Primer. http://www.lecb.ncifcrf.gov/
~toms/paper/primer (April 14, 2007)
7. Cherry, C.: On Human Communication, 2nd edn. MIT Press, Cambridge (1966)
8. MacKay, D.: Information, Mechanism and Meaning. MIT Press, Cambridge (1969)
9. Saraiya, P., North, C., Duka, K.: An evaluation of microarray visualization tools for bio-
logical insight. In: Proc. IEEE Symposium on Information Visualization, pp. 1-8 (2004)
10. Keller, P., Keller, M.: Visual cues: Practical Data Visualization. IEEE Computer Society
Press, Los Alamitos (1993)
11. Cui, Q., Ward, M., Rundensteiner, E., Yang, J.: Measuring data abstraction quality in
multiresolution visualization. In: Proc. IEEE Symposium on Information Visualization, pp.
709-716 (2006)
12. Bertin, J.: Matrix theory of graphics. Information Design 10(1), 5-19 (2001)
13. Seo, J., Shneiderman, B.: A rank-by-feature framework for interactive exploration of multi-
dimensional data. In: Proc. IEEE Symposium on Information Visualization, pp. 96-113
(2005)
14. Peng, W., Ward, M., Rundensteiner, E.: Clutter reduction in multi-dimensional data visu-
alization using dimension reordering. In: Proc. IEEE Symposium on Information Visuali-
zation, pp. 89-96 (2004)
Search WWH ::




Custom Search