Interactive visualization of big data leveraging databases for scalable computation
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2014
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| Online Access: | http://hdl.handle.net/1721.1/84906 |
| _version_ | 1826195936189087744 |
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| author | Battle, Leilani Marie |
| author2 | Michael R. Stonebraker and Samuel R. Madden. |
| author_facet | Michael R. Stonebraker and Samuel R. Madden. Battle, Leilani Marie |
| author_sort | Battle, Leilani Marie |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013. |
| first_indexed | 2024-09-23T10:18:10Z |
| format | Thesis |
| id | mit-1721.1/84906 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:18:10Z |
| publishDate | 2014 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/849062019-04-11T07:07:18Z Interactive visualization of big data leveraging databases for scalable computation Battle, Leilani Marie Michael R. Stonebraker and Samuel R. Madden. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (pages 55-57). Modern database management systems (DBMS) have been designed to efficiently store, manage and perform computations on massive amounts of data. In contrast, many existing visualization systems do not scale seamlessly from small data sets to enormous ones. We have designed a three-tiered visualization system called ScalaR to deal with this issue. ScalaR dynamically performs resolution reduction when the expected result of a DBMS query is too large to be effectively rendered on existing screen real estate. Instead of running the original query, ScalaR inserts aggregation, sampling or filtering operations to reduce the size of the result. This thesis presents the design and implementation of ScalaR, and shows results for two example applications, visualizing earthquake records and satellite imagery data, stored in SciDB as the back-end DBMS. by Leilani Marie Battle. S.M. 2014-02-10T17:01:30Z 2014-02-10T17:01:30Z 2013 Thesis http://hdl.handle.net/1721.1/84906 868904018 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 57 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Battle, Leilani Marie Interactive visualization of big data leveraging databases for scalable computation |
| title | Interactive visualization of big data leveraging databases for scalable computation |
| title_full | Interactive visualization of big data leveraging databases for scalable computation |
| title_fullStr | Interactive visualization of big data leveraging databases for scalable computation |
| title_full_unstemmed | Interactive visualization of big data leveraging databases for scalable computation |
| title_short | Interactive visualization of big data leveraging databases for scalable computation |
| title_sort | interactive visualization of big data leveraging databases for scalable computation |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/84906 |
| work_keys_str_mv | AT battleleilanimarie interactivevisualizationofbigdataleveragingdatabasesforscalablecomputation |