This article describes a perceptual level-of-detail approach for visualizing data. Properties of a dataset that cannot be resolved in the current display environment need not be shown, for example, when too few pixels are used to render a data element, or when the element's subtended visual angle falls below the acuity limits of our visual system. To identify these situations, we asked: (1) What type of information can a human user perceive in a particular display environment? (2) Can we design visualizations that control what they represent relative to these limits? and (3) Is it possible to dynamically update a visualization as the display environment changes, to continue to effectively utilize our perceptual abilities? To answer these questions, we conducted controlled experiments that identified the pixel resolution and subtended visual angle needed to distinguish different values of luminance, hue, size, and orientation. This information is summarized in a perceptual display hierarchy, a formalization describing how many pixels— resolution —and how much physical area on a viewer's retina— visual angle —is required for an element's visual properties to be readily seen. We demonstrate our theoretical results by visualizing historical climatology data from the International Panel for Climate Change.

A big challenge in studying file and storage systems is discovering interrelationships between multiple performance metrics. It is difficult to analyze and interpret this abstract collection of multidimensional performance data using traditional non-visual techniques. This project presents a visualization technique to convert storage controller performance data into an image that domain experts can use for exploring, discovering, comparing, validating, accounting, monitoring, identifying faults, and studying the effects of adjusting different performance parameters. We present an algorithm that visualizes storage controller's performance metrics along a traditional 2D grid or a linear space-filling spiral. We use graphical "glyphs" (simple geometric objects) that vary in color, spatial placement and texture properties to represent the attribute values contained in a data element to form visual structures.