Data Facilities in Visualization Systems

Reference: Chapter 3, Brodlie, etal, "Scientific Visualization: Techniques and Applications, Springer-Verlag, 1992

Introduction

One of the major problems in science and other areas is the massive amount of data that is collected or generated. Visualization systems must be able to enter, store, and retrieve this data. The data also must be converted into one or more internal formats for the visualization system.

This section discusses a taxonomy of different types of data flow, data formats for import/export, data compression techniques (including image compression techniques), and the facilities necessary for managing data.

Data Sources

Data Sources can be external or internal to the system. External sources would be data collected from experimental measurement or generated by a simulation external to the system. Internal sources would be data that was passed between modules, generated by simulations internal to the system, or stored and then retrieved from the system.

Visualization systems should be able to support the real time collection, storage, and analysis of data.

Data Classification

Data Management

Data Management is a very important issue in data visualization. As scientists collect or compute large amounts of raw data, and then generate derived data, reports, images, etc., the management of this becomes crucial. Scientists have not used database management systems (DBMS) much because most DBMS have been developed for commercial use and are not appropriate for data visualization because of the following reasons:

A promising development is the emergence of Object-Oriented DBMS. In these systems, an object could consist of all the different types of data that are associated with a particular experiment or set of experiments. Distributed (over a network) OO-DBMS are also becoming available.

Data Transformation

Before or during an analysis of raw data the user will usually want to perform some type of transformation on the data. There are many types of data transformations, as discussed below.

Feature Detection, Enhancement, and Extraction are different image processing techniques.

Data Compression

A huge volume of data may be generated from the initial experiment or computation and from further data analysis. For purposes of storage or transmission to another system, this data must be compressed.

Here is a further discussion of data compression and image file formats.


HyperVis Table of Contents

Last modified on February 11, 1999, G. Scott Owen, owen@siggraph.org