Massive dataset sizes can make visualization difficult or impossible. One solution to this problem is to divide a
dataset into smaller pieces and then stream these pieces through memory, running algorithms on each piece. This
paper presents a modular data-flow visualization system architecture for culling and prioritized data streaming.
This streaming architecture improves program performance both by discarding pieces of the input dataset that
are not required to complete the visualization, and by prioritizing the ones that are. The system supports a
wide variety of culling and prioritization techniques, including those based on data value, spatial constraints, and
occlusion tests. Prioritization ensures that pieces are processed and displayed progressively based on an estimate
of their contribution to the resulting image. Using prioritized ordering, the architecture presents a progressively
rendered result in a significantly shorter time than a standard visualization architecture. The design is modular,
such that each module in a user-defined data-flow visualization program can cull pieces as well as contribute to
the final processing order of pieces. In addition, the design is extensible, providing an interface for the addition
of user-defined culling and prioritization techniques to new or existing visualization modules.
Rendering a lot of data results in cluttered visualizations. It is difficult for a user to find regions of interest from contextual data especially when occlusion is considered. We incorporate animations into visualization by adding positional motion and opacity change as a highlighting mechanism. By leveraging our knowledge on motion perception, we can help a user to visually filter out her selected data by rendering it with animation. Our framework of adding animation is the animation transfer function, where it provides a mapping from data and animation frame index to a changing visual property. The animation transfer function describes animations for user selected regions of interest. In addition to our framework, we explain the implementation of animations as a modification of the rendering pipeline. The animation rendering pipeline allows us to easily incorporate animations into existing software and hardware based volume renderers.
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