483 research outputs found

    The State-of-the-Art of Set Visualization

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    Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net

    A comparative study of linear and region based diagrams

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    There are two categories of objects spatial information science investigates: actual objects and their spatial properties, such as in geography, and abstract objects which are employed metaphorically, as for visual languages. A prominent example of the latter are diagrams that model knowledge of some domain. Different aspects of diagrams are of interest, including their formal properties or how human users work with them, for example, with diagrams representing sets. The literature about diagrammatic systems for the representation of sets shows a dominance of region-based diagrams like Euler circles and Venn diagrams. The effectiveness of these diagrams, however, is limited because region-based diagrams become quite complex for more then three sets. By contrast, linear diagrams are not equally prevalent but enable the representation of a greater number of sets without getting cluttered. Cluttered diagrams exhibit inherent complexity due to overlapping objects, irrelevant details, or other reasons that impinge upon their legibility. This study contrasts both types of diagrammatic systems and investigates whether the performance of users differs for both kinds of diagrams. A significant difference can be shown regarding the number of diagrams that can be drawn within a fixed period of time and regarding the number of errors made. The results indicate that linear diagrams are more effective by being more restrictive and because region based diagrams show much clutter due to overlapping, coincident, and tangentially touching contours, as well as an overwhelming number of empty zones. Linear diagrams are less prone to errors and do not suffer from clutter

    How to Display Group Information on Node-Link Diagrams: An Evaluation

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    We present the results of evaluating four techniques for displaying group or cluster information overlaid on node-link diagrams: node coloring, GMap, BubbleSets, and LineSets. The contributions of the paper are three fold. First, we present quantitative results and statistical analyses of data from an online study in which approximately 800 subjects performed 10 types of group and network tasks in the four evaluated visualizations. Specifically, we show that BubbleSets is the best alternative for tasks involving group membership assessment; that visually encoding group information over basic node-link diagrams incurs an accuracy penalty of about 25 percent in solving network tasks; and that GMap's use of prominent group labels improves memorability. We also show that GMap's visual metaphor can be slightly altered to outperform BubbleSets in group membership assessment. Second, we discuss visual characteristics that can explain the observed quantitative differences in the four visualizations and suggest design recommendations. This discussion is supported by a small scale eye-tracking study and previous results from the visualization literature. Third, we present an easily extensible user study methodology

    Visualization of modular structures in biological networks

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    The Perception of Clutter in Linear Diagrams

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