Publication Date

2015

Document Type

Thesis

Committee Members

Derek Doran (Committee Member), Michael Raymer (Committee Member), Thomas Wischgoll (Advisor)

Degree Name

Master of Science (MS)

Abstract

Visualization is an important task in data analytics, as it allows researchers to view abstract patterns within the data instead of reading through extensive raw data. Allowing the ability to interact with the visualizations is an essential aspect since it provides the ability to intuitively explore data to find meaning and patterns more efficiently. Interactivity, however, becomes progressively more difficult as the size of the dataset increases. This project begins by leveraging existing web-based data visualization technologies and extends their functionality through the use of parallel processing. This methodology utilizes state-of-the-art techniques, such as Node.js, to split the visualization rendering and user interactivity controls between a client-server infrastructure. The approach minimizes data transfer by performing the rendering step on the server while allowing for the use of HPC systems to render the visualizations more quickly. In order to improve the scaling of the system with larger datasets, parallel processing and visualization optimization techniques are used.

Page Count

46

Department or Program

Department of Computer Science and Engineering


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