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Proceedings Paper

A flexible low-complexity device adaptation approach for data presentation
Author(s): René Rosenbaum; Alfredo Gimenez; Heidrun Schumann; Bernd Hamann
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Paper Abstract

Visual data presentations require adaptation for appropriate display on a viewing device that is limited in re- sources such as computing power, screen estate, and/or bandwidth. Due to the complexity of suitable adaptation, the few proposed solutions available are either too resource-intensive or in exible to be applied broadly. Eective use and acceptance of data visualization on constrained viewing devices require adaptation approaches that are tailored to the requirements of the user and the capabilities of the viewing device. We propose a predictive device adaptation approach that takes advantage of progressive data renement. The approach relies on hierarchical data structures that are created once and used multiple times. By incrementally reconstructing the visual presentation on the client with increasing levels of detail and resource utilization, we can determine when to truncate the renement of detail so as to use the resources of the device to their full capacities. To determine when to nish the renement for a particular device, we introduce a prole-based strategy which also considers user preferences. We discuss the whole adaptation process from the storage of the data into a scalable structure to the presentation on the respective viewing device. This particular implementation is shown for two common data visualization methods, and empirical results we obtained from our experiments are presented and discussed.

Paper Details

Date Published: 24 January 2011
PDF: 12 pages
Proc. SPIE 7868, Visualization and Data Analysis 2011, 78680F (24 January 2011); doi: 10.1117/12.871975
Show Author Affiliations
René Rosenbaum, Univ. of California, Davis (United States)
Alfredo Gimenez, Univ. of California, Davis (United States)
Heidrun Schumann, Univ. Rostock (Germany)
Bernd Hamann, Univ. of California, Davis (United States)


Published in SPIE Proceedings Vol. 7868:
Visualization and Data Analysis 2011
Pak Chung Wong; Jinah Park; Ming C. Hao; Chaomei Chen; Katy Börner; David L. Kao; Jonathan C. Roberts, Editor(s)

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