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Open Access Publications from the University of California

Query-Driven Visualization of Large Data Sets


We present a practical and general-purpose approach to large and complex visual data analysis where visualization processing, rendering and subsequent human interpretation is constrained to the subset of data deemed interesting by the user. In many scientific data analysis applications, "interesting" data can be defined by compound Boolean range queries of the form (temperature > 1000) AND (70 < pressure < 90). As data sizes grow larger, a central challenge is to answer such queries as efficiently as possible. Prior work in the visualization community has focused on answering range queries for scalar fields within the context of accelerating the search phase of isosurface algorithms. In contrast, our work describes an approach that leverages state-of-the-art indexing technology from the scientific data management community called "bitmap indexing." Our implementation, which we call "DEX" (short for dextrous data explorer), uses bitmap indexing to efficiently answer multivariate, multidimensional data queries to provide input to a visualization pipeline. We present an analysis overview and bench-mark results that show bitmap indexing offers significant storage and performance improvements when compared to previous approaches for accelerating the search phase of isosurface algorithms. More importantly, since bitmap indexing supports complex multi-dimensional, multivariate range queries, it is more generally applicable to scientific data visualization and analysis problems. In addition to benchmark performance and analysis, we apply DEX to a typical scientific visualization problem encountered in combustion simulation data analysis. © 2005 IEEE.

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