Graph Visualization

Why is graph visualization an integral part in science? Studies have shown that the majority of people are more comfortable in comprehending knowledge visually. In easier terms we tend to remember things that we see. The television is more appealing than the radio because of its visual element. When we use the representation of data by graph comparison it gets easier to understand. For example, when we see the graph in SENSEX going up, it means state of the market is high. In an office when we see an upwards rising graphical line running from left to right then we shall understand that it is referring to business growth. When we look at the coordinates then shall we fathom out on the hike parameters.

There is a tendency to dismiss the effect of graph visualization as another pretty tool. The argument stands out that graph only shows existing data. Well, it is partially correct. If a graphical chart is thoroughly depicted then it can show data form which would otherwise have been unfeasible to figure out in the raw shape.

GVF or Graph Visualization Framework (i.e. a multi platform java based open source framework) is an important tool in manipulating the graphs to represent visually. It is an example set of design patterns and intuitive approaches to render the data visually. Different modules are created to work with existing application platforms. Take the example of ‘Royere”, this is an application built with the help of GVF. It can be expanded as well as altered to suit the requirements of users.

When combining the different elements of graphical illustration into database visualization the resulting data sets provide intuitive information. The main work of visual meta-mining (i.e. data mining result analysis) includes a link with database knowledge representation and interactivity. A meta-mining system will help the analyst to identify a certain feature from graphical visualization and encode it in the database that functions as a knowledge element; this knowledge element will thereby be used for finding similar features in the database.

Graph visualization consists of three basic steps: lay-outing the graph, interaction with different elements of the raw data and animation. Many complex algorithms are used to extract the data and then layout the end result in the form of a hierarchical tree. A second approach of layout is to use nodes to represent the raw data. The tools used to interact are pan, zoom, rotate, scale, translate, filter, distort, link, etc.