Classification of Visualization Techniques

Brodlie,, developed a taxonomy based on the Visualization Technique entities. Other researchers have developed different taxonomies. Bergeron and Grinstein developed a data oriented classification using the concept of a lattice.

We will classify visualization techniques to provide a conceptual framework for understanding the different techniques. This taxonomy may also suggest new and needed visualization technique modules for a given visualization system.

The entity that is created from the data can be expressed as a function F(X). The domain is X = (x1,x2,x3,...xn) and has dimension n. The function, F, can be a scalar, a vector, or a second order or higher tensor. Then the technique classification will be based on the type of function and the dimension of the domain. Time may be one of the independent variables, and then X = (x1, x2, x3,...xn;t). Time varying phenomena are handled using a sequence of frames which are converted into an animation.

For example, a scalar entity (S) with a domain dimension of 5 will be ES5. Note that the entity E is defined on the domain and it yields a result (scalar, vector, or tensor). A point will be denoted by P, a vector by V and a tensor by T. A vector of dimensionality n will be denoted by Vn.

There are three subcases according to the nature of the domain.

Another special case is when we want to show a set of values over some domain, e.g., temperature and pressure over a 3D domain. this notation is E2S3, or for the general case EmSD.

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HyperVis Table of Contents

Last modified on April 05, 1999, G. Scott Owen,