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This paper presents 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.
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