Relational databases provide significant flexibility to organize, store, and manipulate an infinite variety of complex data collections. This flexibility is enabled by the concept of relational data schemas, which allow data owners to easily design custom databases according to their unique needs. However, user interfaces and information visualizations for accessing and utilizing databases have not kept pace with this level of flexibility. Visualizations need to integrate multiple tables and diverse visualization tools into custom solutions. This paper describes advances to Snap-Together Visualization, introduces Visualization Schemas, and presents an extensible system architecture. The Snap model for custom multiple-view visualization establishes an analogy to the relational data model, enabling coordinated data design and visualization design. Visualization Schemas are a natural extension to data schemas, and provide a user interface that enables data owners to rapidly construct and disseminate custom visualizations without programming. The web-based software architecture supports run-time extensibility, enabling end-user integration and dissemination of diverse data and visualization tools from the field.

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Fusion is a web-based system that enables end-users to rapidly and dynamically construct personalized visualization workspaces without programming. Users first use advanced data schemas to link diverse data sources. Then they use visualization schemas to coordinate visualization components and data-mining algorithms according to the unique needs of their data and tasks. They create a custom interactive visualization workspace that can be published on the web. This is accomplished through the Fusion model and user interface that is based on schema concepts that are easy to learn and simple to use.

Breakdown analysis involves decomposing data into subgroups to allow for comparison and identification of problem areas. Good analysis requires the ability to group data based on attributes or values. Breakdown Visualization provides a mechanism to support this analysis through user guided decomposition and exploration of tabular data with a polyarchy structure. This is useful in domains such as sports statistics and corporate financial reports. Breakdown Visualization utilizes a spreadsheet format for comparison of adjacent visualizations.

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