Dynamic reduction of query result sets for interactive visualizaton

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...

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Bibliographic Details
Main Authors: Battle, Leilani (Contributor), Stonebraker, Michael (Contributor), Chang, Remco (Author)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: 2014-10-09T18:58:40Z.
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Online Access:Get fulltext
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100 1 0 |a Battle, Leilani  |e author 
100 1 0 |a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science  |e contributor 
100 1 0 |a Battle, Leilani  |e contributor 
100 1 0 |a Stonebraker, Michael  |e contributor 
700 1 0 |a Stonebraker, Michael  |e author 
700 1 0 |a Chang, Remco  |e author 
245 0 0 |a Dynamic reduction of query result sets for interactive visualizaton 
260 |c 2014-10-09T18:58:40Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/90853 
520 |a 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 paper presents the design and implementation of ScalaR, and shows results for an example application, displaying satellite imagery data stored in SciDB as the back-end DBMS. 
546 |a en_US 
655 7 |a Article 
773 |t 2013 IEEE International Conference on Big Data