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In the field of information visualization, researchers and developers have created
many types of visualizations, or visual depictions of information. User interface
designers often coordinate multiple visualizations, taking advantage of the strengths of
each, to enable users to rapidly explore complex information. However, the
combination of visualizations and coordinations needed in any given situation depends
heavily on the data, tasks, and users. Consequently, the number of needed
combinations explodes, and implementation becomes intractable.
Snap-Together Visualization (Snap) is a conceptual model, user interface, software
architecture, and implemented system that enables users to rapidly and dynamically
construct coordinated-visualization interfaces, customized for their data, without
programming. Users load data into desired visualizations, then create coordinations
between them, such as brushing and linking, overview and detail, and drill down.
This dissertation presents four primary contributions. First, Snap formalizes a
conceptual model of visualization coordination that is based on the relational data
model. Visualizations display relations, and coordinations tightly couple user
interaction across relational joins.
Second, Snap's user interface enables the construction of coordinated-visualization
interfaces without programming. Data users can dynamically mix and match
visualizations and coordinations while exploring. Data disseminators can distribute
appropriate interfaces with their data. Interface designers can rapidly prototype many
alternatives.
Third, Snap's software architecture enables flexibility in data, visualizations, and
coordinations. Visualization developers can easily snap-enable their independent
visualizations using a simple API, allowing users to coordinate them with many other
visualizations.
Fourth, empirical studies of Snap reveal benefits, cognitive issues, and usability
concerns. Six data-savvy users successfully, enthusiastically, and rapidly designed
powerful coordinated-visualization interfaces of their own. In a study with 18 subjects,
an overview-and-detail coordination reliably improved user performance by 30-80%
over detail-only and uncoordinated interfaces for most tasks.
Snap has proven useful in a variety of domains, including census statistics and
geography, digital photo libraries, case-law documents, web-site logs, and traffic
incident data.
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