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In a data visualization system, data are converted into information and/or knowledge by human vision perception of graph representing these data. This research studies the characteristics of human vision perception and the relationships between data variables of dataset to constitute cube models representing dataset as a graph on the cube. The research proposes the process designing multidimensional graph considered as structural graph on cube model to represent multivariate data. Structural graph is designed in the light of the relationships between data variables and human vision perception. After mapping structural graph onto computer screen, the graph is integrated into retinal variables to improve objectively visual features and respond subjectively human perception of vision.

In recent years, various approaches to visualization have been adopted for decoding the significance implicit in data. Although the effectiveness of these approaches has been evaluated, scant attention has been paid to their efficiency. In this study, an interactive system was designed based on a two-component visualization system. In the proposed model, resource libraries were installed on a computer, and correlations were drawn between human libraries and user characteristics. An interactive visualization system can be used to create different visualization problems. For each problem, the input is a dataset, and the output is the information or knowledge gleaned by the user from the visual pattern representing the dataset. Each visualization problem may be solved using many methods and various visual patterns, with varying cognitive loads.The efficiency of a solution is evaluated by comparing the output worth with the cognition cost of the solution. In this article, a quantitative algorithm was proposed to evaluate the efficiency of a visualization solution to enable the user in determining the most efficient solution according to the purpose of the visualization.

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