CloudVista: visual cluster exploration for extreme scale data in the cloud

  • Authors:

  • Keke Chen;Huiqi Xu;Fengguang Tian;Shumin Guo

  • Affiliations:

  • Ohio Center of Excellence in Knowledge Enabled Computing, Department of Computer Science and Engineering, Wright State University, Dayton, OH;Ohio Center of Excellence in Knowledge Enabled Computing, Department of Computer Science and Engineering, Wright State University, Dayton, OH;Ohio Center of Excellence in Knowledge Enabled Computing, Department of Computer Science and Engineering, Wright State University, Dayton, OH;Ohio Center of Excellence in Knowledge Enabled Computing, Department of Computer Science and Engineering, Wright State University, Dayton, OH

  • Venue:
  • SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
  • Year:
  • 2011

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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 Cloud-Vista 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.