Data visualization refers to the visual representation of data, defined as information which has been abstracted in some schematic form, including attributes or variables for the units of information. With the rapid increase of computing power, larger and more complex numerical models for visualization have been developed, resulting in the generation of huge numerical data sets. Also, large data sets were generated by data acquisition devices such as medical scanners and microscopes, where data has been collected in large databases containing text, numerical information, and multimedia information. Thus, advanced computer graphics techniques were needed to process and visualize such data sets.
Visualization is often considered a part of a process of scientific computing that includes the use of computer modeling and simulation in scientific and engineering practice. More recently, visualization has increasingly been concerned with data from other sources, including large and heterogeneous data collections found in business and finance, administration, digital media, and so forth. A new research area called Information Visualization was launched in the early 1990s, to support analysis of abstract and heterogeneous data sets in many application areas.
Generally, data visualization is an evolving concept where definitional boundaries are continually expanding and, as such, is best defined in terms of loose generalizations. It refers to the more technologically advanced techniques, which allow visual interpretation of data through the representation, modeling and display of solids, surfaces, properties and animations. This involves the use of graphics, image processing, computer vision, and user interfaces. Data visualization also encompasses a much broader range of techniques than specific techniques such as solid modeling, for example. The success of data visualization is due to the soundness of the basic idea behind the technology including the use of computer-generated images to gain insight and knowledge from data and its inherent patterns and relationships. A second premise is the utilization of the broad bandwidth of the human sensory system in steering and interpreting complex processes, and simulations involving data sets from diverse scientific disciplines having large collections of abstract data from many sources. These concepts are important and have a profound and widespread impact on the methodology of computational science and engineering, as well as on management and administration.
The interplay between various application areas and specific problem solving visualization techniques has been the focus of much research and innovation. One area involves the interaction between humans and a generated visualization. For example, if a data visualization of a chart or graph were projected on to a surface, how could a human being interact with the projection and more importantly, what would be the consequences of such interaction. For instance, if a projected chart were manipulated in such a manner as to cause a change in a data relationship that was currently displayed. Present systems would require manual intervention to analyze the suggested change, locate the data source to update the respective change, and to finally manually update the data source with the proposed change. As can be appreciated, these processes for effecting changes in a data structure or relationship are highly inefficient.