The paper presents an interactive approach for guiding the user's select of color-maps in visualization. PRAVDAColor, implemented as a module in the IBM Visualization Data Explorer, provides the user a selection of appropriate color-maps given the data type and spatial frequency, the user's task, and properties of the human perceptual system. 1: IntroductionVisualization is a process of mapping data onto visual dimensions to create a pictorial representation. A successful visualization provides a representation which allows the user to gain insight into the structure of the data, or to communicate aspects of this structure effectively [2] [4] [22]. Even with modern visualization systems, which give the user considerable interactive control over the mapping process, it can be difficult to produce an effective visualization. One strategy for improving this situation is to guide the user in the design of a visualization. In our previous work, we have described an interactive rule-based architecture for incorporating such guidance, and have described certain perceptual and cognitive rules which may be relevant [I41 WI 1161. In this paper, we focus on improving the user's selection of colormaps. To do so, we have built a library of colormaps, and a set of perceptual rules for selecting appropriate maps based on the structure of the data and the goal of the visualization. We have encapsulated this rule-based colormap selection process as a tool, PRAVDAColor, in the IBM Visualization Data Explorer software package, and demonstrate how this module can be incorporated into visualization applications involving the mapping of color onto two-and three-dimensional surfaces. This implementation demonstrates the viability of the technique, and provides a testbed for evaluating the rules. In this architecture, each visualization operation can be associated with rules which constrain the set of choices the user is offered. The architecture also provides for linkages between rules that control different visualization operations, with a choice of parameters for one operation constraining choices that are available for others. This network of linked, intelligent operations helps guide the user through the complex process of designing a visualization.In our previous work, we have described the general principles for implementing such an assemblage of rulebased visualization operations. In this paper, we describe a full ,.implementation of one of these operations, colormap selection. In PRAVDAColor, perceptual rules constrain the set of colormaps offered to the user based on system-provided metadata (data type, data range), metadata computed by algorithm (spatial frequency) and metadata provided by the user (the visualization task). This is in contrast to previous rulebased systems for visualization which do not explicitly support user tasks, color perception, or interactivity in the guidance they offer (e.g., [18]). Limitations of Current TechnologyPerhaps the most common operation in visualization is mapping the values ...

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Many graph layout algorithms optimize visual characteristics to achieve useful representations. Implicitly, their goal is to create visual representations that are more intuitive to human observers. In this paper, we asked users to explicitly manipulate nodes in a network diagram to create layouts that they felt best captured the relationships in the data. This allowed us to measure organizational behavior directly, allowing us to evaluate the perceptual importance of particular visual features, such as edge crossings and edge-lengths uniformity. We also manipulated the interior structure of the node relationships by designing data sets that contained clusters, that is, sets of nodes that are strongly interconnected. By varying the degree to which these clusters were masked by extraneous edges we were able to measure observers sensitivity to the existence of clusters and how they revealed them in the network diagram. Based on these measurements we found that observers are able to recover cluster structure, that the distance between clusters is inversely related to the strength of the clustering, and that users exhibit the tendency to use edges to visually delineate perceptual groups. These results demonstrate the role of perceptual organization in representing graph data and provide concrete recommendations for graph layout algorithms.

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