Results 1 - 10
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213
Prefuse: A toolkit for interactive information visualization
- In ACM Human Factors in Computing Systems (CHI
, 2005
"... In this demonstration we present prefuse, an extensible user interface toolkit for building interactive information visualization applications, including node-link diagrams, containment diagrams, and visualizations of unstructured (edge-free) data such as scatter plots and timelines. prefuse data in ..."
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Cited by 358 (7 self)
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In this demonstration we present prefuse, an extensible user interface toolkit for building interactive information visualization applications, including node-link diagrams, containment diagrams, and visualizations of unstructured (edge-free) data such as scatter plots and timelines. prefuse data into visual forms and then manipulating visual data in aggregate, including layout, animation, and distortion routines. The result is a platform for creating scalable, highly-interactive visualizations of large data sets in a modular and principled fashion. We have used prefuse to implement both novel and existing visualizations, validating the toolkit’s power and expressiveness.
Information visualization and visual data mining.
- IEEE Transactions on Visualization and Computer Graphics
, 2002
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Toward a deeper understanding of the role of interaction in information visualization.
- IEEE Transactions on Visualization and Computer Graphics,
, 2007
"... Abstract-Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and ..."
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Cited by 134 (3 self)
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Abstract-Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.
Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation Visualization
- Issue 6, Nov.-Dec. 2008 Page(s):1539
"... Abstract—Scatterplots remain one of the most popular and widely-used visual representations for multidimensional data due to their simplicity, familiarity and visual clarity, even if they lack some of the flexibility and visual expressiveness of newer multidimensional visualization techniques. This ..."
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Cited by 115 (15 self)
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Abstract—Scatterplots remain one of the most popular and widely-used visual representations for multidimensional data due to their simplicity, familiarity and visual clarity, even if they lack some of the flexibility and visual expressiveness of newer multidimensional visualization techniques. This paper presents new interactive methods to explore multidimensional data using scatterplots. This exploration is performed using a matrix of scatterplots that gives an overview of the possible configurations, thumbnails of the scatterplots, and support for interactive navigation in the multidimensional space. Transitions between scatterplots are performed as animated rotations in 3D space, somewhat akin to rolling dice. Users can iteratively build queries using bounding volumes in the dataset, sculpting the query from different viewpoints to become more and more refined. Furthermore, the dimensions in the navigation space can be reordered, manually or automatically, to highlight salient correlations and differences among them. An example scenario presents the interaction techniques supporting smooth and effortless visual exploration of multidimensional datasets. Index Terms—Visual exploration, visual queries, visual analytics, navigation, multivariate data, interaction. 1
Visual exploration of multivariate graphs
- IN PROCEEDINGS OF THE SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS
, 2006
"... This paper introduces PivotGraph, a software tool that uses a new technique for visualizing and analyzing graph structures. The technique is designed specifically for graphs that are “multivariate, ” i.e., where each node is associated with several attributes. Unlike visualizations which emphasize ..."
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Cited by 87 (1 self)
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This paper introduces PivotGraph, a software tool that uses a new technique for visualizing and analyzing graph structures. The technique is designed specifically for graphs that are “multivariate, ” i.e., where each node is associated with several attributes. Unlike visualizations which emphasize global graph topology, PivotGraph uses a simple grid-based approach to focus on the relationship between node attributes and connections. The interaction technique is derived from an analogy with methods seen in spreadsheet pivot tables and in online analytical processing (OLAP). Finally, several examples are presented in which PivotGraph was applied to real-world data sets.
Clutter reduction in multi-dimensional data visualization using dimension reordering
- IEEE InfoVis
, 2004
"... Visual clutter denotes a disordered collection of graphical entities in information visualization. Clutter can obscure the structure present in the data. Even in a small dataset, clutter can make it hard for the viewer to find patterns, relationships and structure. In this paper, we define visual cl ..."
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Cited by 84 (6 self)
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Visual clutter denotes a disordered collection of graphical entities in information visualization. Clutter can obscure the structure present in the data. Even in a small dataset, clutter can make it hard for the viewer to find patterns, relationships and structure. In this paper, we define visual clutter as any aspect of the visualization that interferes with the viewer’s understanding of the data, and present the concept of clutter-based dimension reordering. Dimension order is an attribute that can significantly affect a visualization’s expressiveness. By varying the dimension order in a display, it is possible to reduce clutter without reducing information content or modifying the data in any way. Clutter reduction is a display-dependent task. In this paper, we follow a three-step procedure for four different visualization techniques. For each display technique, first, we determine what constitutes clutter in terms of display properties; then we design a metric to measure visual clutter in this display; finally we search for an order that minimizes the clutter in a display.
Protovis: A Graphical Toolkit for Visualization
"... Abstract—Despite myriad tools for visualizing data, there remains a gap between the notational efficiency of high-level visualization systems and the expressiveness and accessibility of low-level graphical systems. Powerful visualization systems may be inflexible or impose abstractions foreign to vi ..."
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Cited by 84 (6 self)
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Abstract—Despite myriad tools for visualizing data, there remains a gap between the notational efficiency of high-level visualization systems and the expressiveness and accessibility of low-level graphical systems. Powerful visualization systems may be inflexible or impose abstractions foreign to visual thinking, while graphical systems such as rendering APIs and vector-based drawing programs are tedious for complex work. We argue that an easy-to-use graphical system tailored for visualization is needed. In response, we contribute Protovis, an extensible toolkit for constructing visualizations by composing simple graphical primitives. In Protovis, designers specify visualizations as a hierarchy of marks with visual properties defined as functions of data. This representation achieves a level of expressiveness comparable to low-level graphics systems, while improving efficiency—the effort required to specify a visualization—and accessibility—the effort required to learn and modify the representation. We substantiate this claim through a diverse collection of examples and comparative analysis with popular visualization tools. Index Terms—Information visualization, user interfaces, toolkits, 2D graphics. 1
Show me: Automatic presentation for visual analysis
- IEEE Transactions on Visualization and Computer Graphics
, 2007
"... Abstract-This paper describes Show Me, an integrated set of user interface commands and defaults that incorporate automatic presentation into a commercial visual analysis system called Tableau. A key aspect of Tableau is VizQL, a language for specifying views, which is used by Show Me to extend aut ..."
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Cited by 79 (1 self)
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Abstract-This paper describes Show Me, an integrated set of user interface commands and defaults that incorporate automatic presentation into a commercial visual analysis system called Tableau. A key aspect of Tableau is VizQL, a language for specifying views, which is used by Show Me to extend automatic presentation to the generation of tables of views (commonly called small multiple displays). A key research issue for the commercial application of automatic presentation is the user experience, which must support the flow of visual analysis. User experience has not been the focus of previous research on automatic presentation. The Show Me user experience includes the automatic selection of mark types, a command to add a single field to a view, and a pair of commands to build views for multiple fields. Although the use of these defaults and commands is optional, user interface logs indicate that Show Me is used by commercial users.
Interactive Hierarchical Dimension Ordering, Spacing and Filtering for Exploration of High Dimensional Datasets
- Proc. IEEE Symposium on Information Visualization
, 2003
"... Large numbers of dimensions not only cause clutter in multidimensional visualizations, but also make it difficult for users to navigate the data space. Effective dimension management, such as dimension ordering, spacing and filtering, is critical for visual exploration of such datasets. Dimension or ..."
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Cited by 70 (5 self)
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Large numbers of dimensions not only cause clutter in multidimensional visualizations, but also make it difficult for users to navigate the data space. Effective dimension management, such as dimension ordering, spacing and filtering, is critical for visual exploration of such datasets. Dimension ordering and spacing explicitly reveal dimension relationships in arrangement-sensitive multidimensional visualization techniques, such as Parallel Coordinates, Star Glyphs, and Pixel-Oriented techniques. They facilitate the visual discovery of patterns within the data. Dimension filtering hides some of the dimensions to reduce clutter while preserving the major information of the dataset.
Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation
- Proceedings of IEEE Information Visualization 2008, IEEE
, 2008
"... Abstract—Interactive history tools, ranging from basic undo and redo to branching timelines of user actions, facilitate iterative forms of interaction. In this paper, we investigate the design of history mechanisms for information visualization. We present a design space analysis of both architectur ..."
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Cited by 59 (6 self)
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Abstract—Interactive history tools, ranging from basic undo and redo to branching timelines of user actions, facilitate iterative forms of interaction. In this paper, we investigate the design of history mechanisms for information visualization. We present a design space analysis of both architectural and interface issues, identifying design decisions and associated trade-offs. Based on this analysis, we contribute a design study of graphical history tools for Tableau, a database visualization system. These tools record and visualize interaction histories, support data analysis and communication of findings, and contribute novel mechanisms for presenting, managing, and exporting histories. Furthermore, we have analyzed aggregated collections of history sessions to evaluate Tableau usage. We describe additional tools for analyzing users ’ history logs and how they have been applied to study usage patterns in Tableau. Index Terms—Visualization, history, undo, analysis, presentation, evaluation. 1
