Query-driven visualization of large data sets

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Query-driven visualization of large data sets (2005)

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by Kurt Stockinger , John Shalf , Kesheng Wu , E. Wes Bethel

Venue:	In VIS ’05: Proceedings of the conference on Visualization ’05
Citations:	49 - 15 self

BibTeX

@INPROCEEDINGS{Stockinger05query-drivenvisualization,
    author = {Kurt Stockinger and John Shalf and Kesheng Wu and E. Wes Bethel},
    title = {Query-driven visualization of large data sets},
    booktitle = {In VIS ’05: Proceedings of the conference on Visualization ’05},
    year = {2005},
    pages = {167--174}
}

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Abstract

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 benchmark 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 multidimensional, 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.

Keyphrases

query-driven visualization large data set search phase isosurface algorithm range query visualization processing general-purpose approach benchmark result bitmap indexing multivariate range query dextrous data explorer many scientific data analysis application central challenge analysis overview complex visual data analysis offer significant storage scientific data visualization scalar field data size support complex multidimensional subsequent human interpretation visualization pipeline performance improvement analysis problem prior work combustion simulation data analysis compound boolean range query typical scientific visualization problem visualization community multidimensional data query scientific data management community previous approach

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