Analysis and visualization of large data sets is time consuming and sometimes can be a very difficult process, especially for 3D data sets. Therefore, data processing and visualization techniques have often been used in the case of different massive data analysis for efficiency and accuracy purposes. This thesis presents a multi-scale data sketching solution, specifically for large 3D scientific data with a goal to support collaborative data management, analysis and visualization. The idea is to allow users to quickly identify interesting regions and observe significant patterns without directly accessing the raw data, since most of the information in raw form is not useful. This solution will provide a fast way to allow the users to choose the regions they are interested and save time. By preprocessing the data, our solution can sketch out the general regions of the 3D data, and users can decide whether they are interested in going further to analyze the current data. The key issue is to find efficient and accurate algorithms to detect boundaries or regions information for large 3D scientific data. Specific techniques and performance analysis are also discussed.
| Identifer | oai:union.ndltd.org:pacific.edu/oai:scholarlycommons.pacific.edu:uop_etds-1831 |
| Date | 01 January 2012 |
| Creators | Song, Huaguang |
| Publisher | Scholarly Commons |
| Source Sets | University of the Pacific |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of the Pacific Theses and Dissertations |
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