Transformations for volumetric range distribution queries

Abstract

Volumetric datasets continue to grow in size, and there is continued demand for interactive analysis on these datasets. Because storage device throughputs are not increasing as quickly, interactive analysis workflows are becoming working set-constrained. In an ideal workflow, the working set complexity of the interactive analysis portion of the workflow should depend primarily on the size of the analysis result being produced, rather than on the size of the data being analyzed. Past works in online analytical processing and visualization have addressed this problem within application-specific contexts, but have not generalized their solutions to a wider variety of visualization applications. We propose a general framework for reducing the working set complexity of the interactive portion of visualization workflows that can be built on top of distribution range queries, as well as a technique within this framework able to support multiple visualization applications. Transformations are applied in the preprocessing phase of the workflow to enable fast, approximate volumetric distribution range queries with low working set complexity. Interactive application algorithms are then adapted to make use of these distribution range queries, enabling efficient interactive workflows on large-scale data. We show that the proposed technique enables these applications to be scaled primarily in terms of the application result dataset size, rather than the input data size, enabling increased interactivity and scalability.