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Open Access Publications from the University of California

Query-Driven Visualization of Large Data Sets


We present a practical approach to large and complex visual data analysis where visualization processing, rendering and subsequent human interpretation is restricted to data deemed interesting by the user. Our implementation reduces processing load by using efficient, multi-dimensional bitmap indexing technology to extract data subsets from large, complex data stores. Data is selectively processed based upon such query expressions as (temperature > 100) AND (70 < pressure < 90) that a user specifies to indicate data that "is interesting." Such a query-driven approach offers advantages over scalable technologies aimed at visualizing ever-larger datasets. Namely, less time is spent visualizing and interpreting data that is not of interest. In this paper, we introduce our implementation, called "DEX" (short for dexterous data explorer), which integrates bitmap indexing and region growing technology with a 3D visualization processing pipeline. We provide benchmark results that demonstrate DEX's performance efficiency relative to commonly used visualization methods. Finally, we show how DEX is used to apply query driven visualization methods to demanding visualization problems in Combustion.

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