Amit P. Sawant

On the limits of resolution and visual angle in visualization

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 elements 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 viewers retina—visual angle—is required for an elements visual properties to be readily seen. We demonstrate our theoretical results by visualizing historical climatology data from the International Panel for Climate Change.

Visualizing multidimensional query results using animation

Effective representation of large, complex collections of information (datasets) presents a difficult challenge. Visualization is a solution that uses a visual interface to support efficient analysis and discovery within the data. Our primary goal in this paper is a technique that allows viewers to compare multiple query results representing user-selected subsets of a multidimensional dataset. We present an algorithm that visualizes multidimensional information along a space-filling spiral. Graphical glyphs that vary their position, color, and texture appearance are used to represent attribute values for the data elements in each query result. Guidelines from human perception allow us to construct glyphs that are specifically designed to support exploration, facilitate the discovery of trends and relationships both within and between data elements, and highlight exceptions. A clustering algorithm applied to a user-chosen ranking attribute bundles together similar data elements. This encapsulation is used to show relationships across different queries via animations that morph between query results. We apply our techniques to the MovieLens recommender system, to demonstrate their applicability in a real-world environment, and then conclude with a simple validation experiment to identify the strengths and limitations of our design, compared to a traditional side-by-side visualization.

Designing a visualization framework for multidimensional data

Interest in visualization has grown in recent years, producing rapid advances in the diversity of research and in the scope of proposed techniques. Much of the initial focus in computer-based visualization concentrated on display algorithms, often for specific domains. For example, volume, flow, and terrain visualization techniques have generated significant insights into fundamental graphics and visualization theory, aiding the application experts who use these techniques to advance their own research. More recent work has extended visualization to abstract data sets like network intrusion detection, recommender systems, and database query results. This article describes our initial end-to-end system that starts with data management and continues through assisted visualization design, display, navigation, and user interaction. The purposes of this discussion are to (i) promote a more comprehensive visualization framework; (ii) describe how to apply expertise from human psychophysics, databases, rational logic, and artificial intelligence to visualization; and (iii) illustrate the benefits of a more complete framework using examples from our own experiences.

Visualizing flow data using assorted glyphs

This project visualizes a scientific dataset containing two-dimensional flow data from a simulated supernova collapse provided by astrophysics researchers. We started our project by designing visualizations using multiple hand drawings representing the flow data without taking into consideration the implementation constraints of our designs. We implemented a few of our hand drawn designs. We used an assortment of simple geometric graphical objects, called glyphs, such as, dots, lines, arrows, and triangles to represent the flow at each sample point. We also incorporated transparency in our visualizations. We identified two important goals for our project: (1) design different types of graphical glyphs to support flexibility in their placement and in their ability to represent multidimensional data elements, and (2) build an effective visualization technique that uses glyphs to represent the two-dimensional flow field.

AmazonViz: visualizing Amazon.com purchase orders

This article describes a technique to visualize query results, representing purchase orders placed on Amazon.com, along a traditional 2-D scatter plot and a space-filling spiral. We integrate 3-D objects that vary their spatial placement, color, and texture properties into a visualization algorithm. This algorithm represents important aspects of a purchase order based on experimental results from human vision, computer graphics, and psychology. The resulting visual abstractions are used by viewers to rapidly and effectively explore and analyze the underlying purchase orders data.

MultiVizArch: multiple graphical layouts for visualizing software architecture

This article presents an automated technique for visualizing large software architectures using multiple graphical representations, including multi-dimensional scaling, 2-D grid, and spiral layouts. We describe how our software visualization methods were applied to the Network Appliance operating system known as Data ONTAP 7G (ONTAP). We show how each method can be applied to comprehend a specific aspect of ONTAP. This approach can be used by software engineers, architects, and developers to better understand the architecture of their code.