Information visualisation is the visualisation of information :-)
Information visualisation is concerned with creating visual, understandable representations of data. The data may or may not be geometric in nature, it may have many observations, it may have high dimensionality and it may be relational or non-relational.
The goal is use a visualisation (like a graph) in a process of visual data mining to uncover new, interesting information about the data. My thesis centres on the use of Virtual Environments (VEs) for the process of data visualisation. I am primarily concerned with the visualisation of multidimensional, non-relational, non-geometric real-valued data sets. Put differently, I am visualising n dimensional vectors from an n dimensional data space.
Nagappan R., Lin T. A Virtual Environment for Exploring
Relational Information. Proceedings of SimTecT'99,
30 Mar - 1 Apr, 1999. pp 313 - 318.
Microsoft Word
Nagappan R. A Compositional Model for Multidimensional Data Visualisation. Visual Data Exploration and Analysis VIII. San Jose, USA. In Proceedings of SPIE, vol. 4302. January 2001. PDF
Nagappan R. Employing Virtual Enviroments for Data Visualisation. Proceedings of SimTecT 2001. Canberra, Australia. May 2001. Microsoft Word
Nagappan R. Exploring Visualisations through Subspace Composition. Proceedings of the First Australasian User Interface Conference. Canberra, Australia. February 2000. PDF
Nagappan R. A Graph-based Model for Navigating Visualisation. Visual Information Processing 2000. Sydney, Australia. December 2000. http://www.cs.usyd.edu.au/~vip2000/ gzipped postscript
Nagappan R. Visualising Multidimensional Non-Geometric Data Sets. Visual Data Exploration and Analysis VII. San Jose, USA. In Proceedings of SPIE, vol. 3960, pp 26-34. January 2000. PDF
mdOrb is a tool for visualising multidimensional relational data using VE technology. mdOrb is available free for non-profit use only. It is provided without warranty or support. You will need Open Inventor and OSF/ Motif to compile the source. The distribution includes all data and generation scripts for the feature finding tests and MACHO case study in my PhD thesis. Download mdOrb.tar.gz (1.4MB).
For any questions or other uses of mdOrb contact Raj.Nagappan@cs.anu.edu.au