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Visualization of Large-Scale Distributed Data
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Author(s): Jason Leigh (University of Illinois at Chicago, USA), Andrew Johnson (University of Illinois at Chicago, USA), Luc Renambot (University of Illinois at Chicago, USA), Venkatram Vishwanath (University of Illinois at Chicago, USA & Argonne National Laboratory, USA), Tom Peterka (Argonne National Laboratory, USA)and Nicholas Schwarz (Northwestern University, USA)
Copyright: 2012
Pages: 33
Source title:
Data Intensive Distributed Computing: Challenges and Solutions for Large-scale Information Management
Source Author(s)/Editor(s): Tevfik Kosar (University at Buffalo, USA)
DOI: 10.4018/978-1-61520-971-2.ch011
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Abstract
An effective visualization is best achieved through the creation of a proper representation of data and the interactive manipulation and querying of the visualization. Large-scale data visualization is particularly challenging because the size of the data is several orders of magnitude larger than what can be managed on an average desktop computer. Large-scale data visualization therefore requires the use of distributed computing. By leveraging the widespread expansion of the Internet and other national and international high-speed network infrastructure such as the National LambdaRail, Internet-2, and the Global Lambda Integrated Facility, data and service providers began to migrate toward a model of widespread distribution of resources. This chapter introduces different instantiations of the visualization pipeline and the historic motivation for their creation. The authors examine individual components of the pipeline in detail to understand the technical challenges that must be solved in order to ensure continued scalability. They discuss distributed data management issues that are specifically relevant to large-scale visualization. They also introduce key data rendering techniques and explain through case studies approaches for scaling them by leveraging distributed computing. Lastly they describe advanced display technologies that are now considered the “lenses” for examining large-scale data.
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