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Proceedings Paper

Analytics for massive heat maps
Author(s): Shawn J. Bohn; Deborah Payne; Grant Nakamura; Douglass Love
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Paper Abstract

High throughput instrumentation for genomics is producing data orders of magnitude greater than even a decade before. Biologists often visualize the data of these experiments through the use of heat maps. For large datasets, heat map visualizations do not scale. These visualizations are only capable of displaying a portion of the data, making it difficult for scientists to find and detect patterns that span more than a subsection of the data. We present a novel method that provides an interactive visual display for massive heat maps [O(108)]. Our process shows how a massive heat map can be decomposed into multiple levels of abstraction to represent the underlying macrostructures. We aggregate these abstractions into a framework that can allow near real-time navigation of the space. To further assist pattern discovery, we ground our system on the principle of focus+context. Our framework also addresses the issue of balancing the memory and display resolution and heat map size. We will show that this technique for biologists provides a powerful new visual metaphor for analyzing massive datasets.

Paper Details

Date Published: 20 January 2009
PDF: 10 pages
Proc. SPIE 7243, Visualization and Data Analysis 2009, 724303 (20 January 2009); doi: 10.1117/12.811651
Show Author Affiliations
Shawn J. Bohn, Pacific Northwest National Lab. (United States)
Deborah Payne, Pacific Northwest National Lab. (United States)
Grant Nakamura, Pacific Northwest National Lab. (United States)
Douglass Love, Pacific Northwest National Lab. (United States)

Published in SPIE Proceedings Vol. 7243:
Visualization and Data Analysis 2009
Katy Börner; Jinah Park, Editor(s)

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