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Conference Paper: Multiscale visualization of relational databases using layered zoom trees and partial data cubes

TitleMultiscale visualization of relational databases using layered zoom trees and partial data cubes
Authors
KeywordsData Cubes
Database
Gpgpus
H-Tree
Visualization
Issue Date2010
Citation
Imagapp 2010 - Proceedings Of The International Conference On Imaging Theory And Applications, Ivapp 2010 - Proc. Int. Conf. Information Visualization Theory And Applications, 2010, p. 101-111 How to Cite?
AbstractThe analysis and exploration necessary to gain deep understanding of large databases demand an intuitive and informative human-computer interface. In this paper, we present a visualization system with a client-server architecture for multiscale visualization of relational databases. The visual interface on the client supports web-based remote access. We use zoom trees to represent the entire history of a zooming process that reveals multiscale details. Every path in a zoom tree represents a zoom path and every node in the tree can have an arbitrary number of subtrees to support arbitrary branching and backtracking. Zoom trees are seamlessly integrated with a table-based overview using "hyperlinks" embedded in the table. To support fast query processing on the server, we further develop efficient GPU-based parallel algorithms for online data cubing and CPU-based data clustering. Also, a user study was conducted to evaluate the effectiveness of our design.
Persistent Identifierhttp://hdl.handle.net/10722/151977
References

 

DC FieldValueLanguage
dc.contributor.authorWang, Ben_US
dc.contributor.authorChen, Gen_US
dc.contributor.authorBu, Jen_US
dc.contributor.authorYu, Yen_US
dc.date.accessioned2012-06-26T06:31:48Z-
dc.date.available2012-06-26T06:31:48Z-
dc.date.issued2010en_US
dc.identifier.citationImagapp 2010 - Proceedings Of The International Conference On Imaging Theory And Applications, Ivapp 2010 - Proc. Int. Conf. Information Visualization Theory And Applications, 2010, p. 101-111en_US
dc.identifier.urihttp://hdl.handle.net/10722/151977-
dc.description.abstractThe analysis and exploration necessary to gain deep understanding of large databases demand an intuitive and informative human-computer interface. In this paper, we present a visualization system with a client-server architecture for multiscale visualization of relational databases. The visual interface on the client supports web-based remote access. We use zoom trees to represent the entire history of a zooming process that reveals multiscale details. Every path in a zoom tree represents a zoom path and every node in the tree can have an arbitrary number of subtrees to support arbitrary branching and backtracking. Zoom trees are seamlessly integrated with a table-based overview using "hyperlinks" embedded in the table. To support fast query processing on the server, we further develop efficient GPU-based parallel algorithms for online data cubing and CPU-based data clustering. Also, a user study was conducted to evaluate the effectiveness of our design.en_US
dc.languageengen_US
dc.relation.ispartofIMAGAPP 2010 - Proceedings of the International Conference on Imaging Theory and Applications, IVAPP 2010 - Proc. Int. Conf. Information Visualization Theory and Applicationsen_US
dc.subjectData Cubesen_US
dc.subjectDatabaseen_US
dc.subjectGpgpusen_US
dc.subjectH-Treeen_US
dc.subjectVisualizationen_US
dc.titleMultiscale visualization of relational databases using layered zoom trees and partial data cubesen_US
dc.typeConference_Paperen_US
dc.identifier.emailYu, Y:yzyu@cs.hku.hken_US
dc.identifier.authorityYu, Y=rp01415en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-77956314219en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77956314219&selection=ref&src=s&origin=recordpageen_US
dc.identifier.spage101en_US
dc.identifier.epage111en_US
dc.identifier.scopusauthoridWang, B=22952181400en_US
dc.identifier.scopusauthoridChen, G=10039149700en_US
dc.identifier.scopusauthoridBu, J=7005200782en_US
dc.identifier.scopusauthoridYu, Y=8554163500en_US

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