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1. If visualization is the solution, what is the problem?

Present day scientific and engineering investigators are confronted with research problems that depend on gaining insight into complex and voluminous data. Previous publications - particularly [McCormick 871 - have referred to

Scientific visualization is devoted to providing visual tools and methods (and some non-visual ones) to help a scientific or engineering investigator with analysing data.

Characterising the investigator's problem

There is no single visualization method or tool that can be applied successfully to all problems of data analysis. Therefore if visualization is intended to assist with demanding problems, it is worthwhile beginning by characterising in what way the investigator's problem is demanding:

For convenience, the characteristics are summarised in the following table.

Table - Characteristics of Investigator's Data
Characteristic Simple Hard
Independent variables 1 Multidimensional
Independent variables 1 Multivariate
Data compounding Scalars Tensors
Geometry Cartesian Curved
Structure Regular Unstructured
Time Static phenomenon Time-varying
Application control None - postprocess Full interactive control steering
Small Vast

Visualization could be said to encompass problems of all types, whether simple or hard.

In practice many traditional solutions (graphs, bar charts) exist where the characteristics of a problem are simple in all respects or where the problem is hard to a limited degree.

The purpose of much recent work in visualization is to investigate the hard problems and bring them into the realm of the possible. In practice the difficultics are interlinked. So, while it is possible to display a field of scalars in 3D space by some suitable volume rendering techniques, it is much harder if the data are vectors, especially if there are many of them - it is easy to display them but hard to perceive them.

As might be expected, there is a gradual adoption of solutions into commercial systems.


Some examples may be useful at this point.

For convenience, these examples are summarised.

Table - Examples
Characteristic Temperature Simple 2D flow Complex 3D flow Chemical process
Independent variables 2 2 3 many
Dependent variables 1 1 1 many
Data compounding scalar vector vector vector
Geometry Cartesian Cartesian Cartesian Cartesian
Structure regular regular unstructured unstructured
Time static static time-varying time-varying

Some characteristics have not been presented in the table. For instance the data set size can be small or large in any of the problems just described.

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