Data Visualization Framework Research Articles

The Role of Visual Perception in Data Visualization

This paper presents a perceptually motivated formal framework for effective visualization of relational data. In this framework, the intended structure of data and the perceptual structure of visualizations are formally and uniformly defined in terms of relations that are induced on data and visual elements by data and visual attributes, respectively. Visual attributes are analyzed and classified from the perceptual point of view and in terms of perceptual relations that they induce on visual elements. The presented framework satisfies a necessary condition for effective data visualizations. This condition is formulated in terms of a structure preserving map between the intended structure of data and the perceptual structure of visualization.

A combined latent class and trait model for the analysis and visualization of discrete data

We present a general framework for data analysis and visualization by means of topographic organization and clustering. Imposing distributional assumptions on the assumed underlying latent factors makes the proposed model suitable for both visualization and clustering. The system noise will be modeled in parametric form, as a member of the exponential family of distributions and this allows us to deal with different (continuous or discrete) types of observables in a unified framework. In this paper, we focus on discrete case formulations which, contrary to self organizing methods for continuous data, imply variants of Bregman divergencies as measures of dissimilarity between data and reference points and, also, define the matching nonlinear relation between latent and observable variables. Therefore, the trait variant of the model can be seen as a data-driven noisy nonlinear independent component analysis, which is capable of revealing meaningful structure in the multivariate observable data and visualizing it in two dimensions. The class variant (which performs the clustering) of our model performs data-driven parametric mixture modeling. The combined (trait and class) model along with the associated estimation procedures allows us to interpret the visualization result, in the sense of a topographic ordering. One important application of this work is the discovery of underlying semantic structure in text-based documents. Experimental results on various subsets of the 20-News groups text corpus and binary coded digits data are given by way of demonstration.

An interactive, discipline-independent data visualization system

Critical to the understanding of data is the ability to provide pictorial or visual representations of that data. Given the ability to access data via an appropriate, easy‐to‐use interface, a researcher must employ tools to visualize data as one important mechanism in the data analysis process. Visualization, then, is a method of computing that gives visual form to complex data utilizing graphics and imaging technology. To support correlative data analysis (i.e., working with data from a variety of sources to study a problem of scientific interest), there is an obvious focus on generic visualization via the development of discipline‐independent visualization techniques. The NSSDC (National Space Science Data Center) Graphics System (NGS) has been developed to provide an interactive discipline‐independent toolbox for nonprogrammers to achieve such visualization of data. The NGS supports displays of any arbitrary multidimensional subset of any data set by providing a large variety of different representation schemes, all of which are supported by implicit animation (i.e., slicing of a data set into sequences). In addition, the design of the NGS provides an open‐ended framework for data visualization, so that new capabilities can be added. New tools have been implemented as a result of NSSDC’s research in several areas of computer science, including novel computer graphics rendering techniques and data structures.