Paper
3 February 2014 Abstract rendering: out-of-core rendering for information visualization
Joseph A. Cottam, Andrew Lumsdaine, Peter Wang
Author Affiliations +
Proceedings Volume 9017, Visualization and Data Analysis 2014; 90170K (2014) https://doi.org/10.1117/12.2041200
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
Abstract
As visualization is applied to larger data sets residing in more diverse hardware environments, visualization frameworks need to adapt. Rendering techniques are currently a major limiter since they tend to be built around central processing with all of the geometric data present. This is not a fundamental requirement of information visualization. This paper presents Abstract Rendering (AR), a technique for eliminating the centralization requirement while preserving some forms of interactivity. AR is based on the observation that pixels are fundamentally bins, and that rendering is essentially a binning process on a lattice of bins. By providing a more flexible binning process, the majority of rendering can be done with the geometric information stored out-of-core. Only the bin representations need to reside in memory. This approach enables: (1) rendering on large datasets without requiring large amounts of working memory, (2) novel and useful control over image composition, (3) a direct means of distributing the rendering task across processes, and (4) high-performance interaction techniques on large datasets. This paper introduces AR in a theoretical context, provides an overview of an implementation, and discusses how it has been applied to large-scale data visualization problems.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joseph A. Cottam, Andrew Lumsdaine, and Peter Wang "Abstract rendering: out-of-core rendering for information visualization", Proc. SPIE 9017, Visualization and Data Analysis 2014, 90170K (3 February 2014); https://doi.org/10.1117/12.2041200
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CITATIONS
Cited by 9 scholarly publications.
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KEYWORDS
Visualization

Information visualization

Associative arrays

Java

Visual analytics

Computer programming

Transform theory

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