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Presentation
CloudVista: Visual Cluster Exploration for Extreme Scale Data in the Could
Lecture Notes in Computer Science
  • Keke Chen, Wright State University - Main Campus
  • Huiqi Xi, Wright State University - Main Campus
  • Fengguang Tian, Wright State University - Main Campus
  • Shumin Guo, Wright State University - Main Campus
Document Type
Conference Proceeding
Publication Date
1-1-2011
Abstract
The problem of efficient and high-quality clustering of extreme scale datasets with complex clustering structures continues to be one of the most challenging data analysis problems. An innovate use of data cloud would provide unique opportunity to address this challenge. In this paper, we propose the CloudVista framework to address (1) the problems caused by using sampling in the existing approaches and (2) the problems with the latency caused by cloud-side processing on interactive cluster visualization. The CloudVista framework aims to explore the entire large data stored in the cloud with the help of the data structure visual frame and the previously developed VISTA visualization model. The latency of processing large data is addressed by the RandGen algorithm that generates a series of related visual frames in the cloud without user's intervention, and a hierarchical exploration model supported by cloud-side subset processing. Experimental study shows this framework is effective and efficient for visually exploring clustering structures for extreme scale datasets stored in the cloud.
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The featured PDF document is the unpublished, peer-reviewed version of this article.

The featured abstract was published in the final version of this article, which appeared in Lecture Notes in Computer Science, volume 6809, pp. 332-350 and may be found at http://link.springer.com/content/pdf/10.1007%2F978-3-642-22351-8_21.pdf .

DOI
10.1007/978-3-642-22351-8_21
Citation Information
Keke Chen, Huiqi Xi, Fengguang Tian and Shumin Guo. "CloudVista: Visual Cluster Exploration for Extreme Scale Data in the Could" Lecture Notes in Computer Science Vol. 6809 (2011) p. 332 - 350 ISSN: 978-3-642-22350-1
Available at: http://works.bepress.com/keke_chen/44/