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Data and information visualization (data viz/vis or info viz/vis) [2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount [3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items.
In databases, brushing and linking is the connection of two or more views of the same data, such that a change to the representation in one view affects the representation in the other. [1] Brushing and linking is also an important technique in interactive visual analysis , a method for performing visual exploration and analysis of large ...
Graphical statistical methods have four objectives: [2] The exploration of the content of a data set; The use to find structure in data; Checking assumptions in statistical models; Communicate the results of an analysis. If one is not using statistical graphics, then one is forfeiting insight into one or more aspects of the underlying structure ...
Visual analytics is especially concerned with coupling interactive visual representations with underlying analytical processes (e.g., statistical procedures, data mining techniques) such that high-level, complex activities can be effectively performed (e.g., sense making, reasoning, decision making).
The techniques rely heavily on user interaction and the human visual system, and exist in the intersection between visual analytics and big data. It is a branch of data visualization. IVA is a suitable technique for analyzing high-dimensional data that has a large number of data points, where simple graphing and non-interactive techniques give ...
Scientific visualization focuses and emphasizes the representation of higher order data using primarily graphics and animation techniques. [ 5 ] [ 6 ] It is a very important part of visualization and maybe the first one, as the visualization of experiments and phenomena is as old as science itself.
Visual computing [1] is a fairly new term, which got its current meaning around 2005, when the International Symposium on Visual Computing first convened. [2] Areas of computer technology concerning images, such as image formats, filtering methods, color models, and image metrics, have in common many mathematical methods and algorithms.
According to Chi (2000), he and J.T. Reidl "in 1998 extends and proposes a new way to taxonomize information visualization techniques by using the Data State Model. Many of the techniques share similar operating steps that can easily be reused. The Data State Model not only helps researchers understand the space of design, but also helps ...