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Conference Paper: ZoomTree: Unrestricted zoom paths in multiscale visual analysis of relational databases

TitleZoomTree: Unrestricted zoom paths in multiscale visual analysis of relational databases
Authors
Issue Date2011
Citation
Communications In Computer And Information Science, 2011, v. 229 CCIS, p. 299-317 How to Cite?
AbstractUnrestricted zoom paths are much desired to gain deep understandings during visual analysis of relational databases. We present a multiscale visualization system supporting unrestricted zoom paths. Our system has a flexible visual interface on the client side, called "ZoomTree", and a powerful and efficient back end with GPU-based parallel online data cubing and CPU-based data clustering. Zoom-trees are seamlessly integrated with a table-based overview using "hyperlinks" embedded in the table, and are designed to represent the entire history of a zooming process that reveals multiscale data characteristics. Arbitrary branching and backtracking in a zoom-tree are made possible by our fast parallel online cubing algorithm for partially materialized data cubes. Partial materialization provides a good tradeoff among preprocessing time, storage and online query time. Experiments and a user study have confirmed the effectiveness of our design. © 2011 Springer-Verlag.
DescriptionBook title: Computer Vision, Imaging and Computer Graphics. Theory and Applications: International Joint Conference, VISIGRAPP 2010, Angers, France, May 17-21, 2010. Revised Selected Papers
Persistent Identifierhttp://hdl.handle.net/10722/152035
ISSN
2014 SCImago Journal Rankings: 0.148
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.accessioned2012-06-26T06:32:43Z-
dc.date.available2012-06-26T06:32:43Z-
dc.date.issued2011en_US
dc.identifier.citationCommunications In Computer And Information Science, 2011, v. 229 CCIS, p. 299-317en_US
dc.identifier.issn1865-0929en_US
dc.identifier.urihttp://hdl.handle.net/10722/152035-
dc.descriptionBook title: Computer Vision, Imaging and Computer Graphics. Theory and Applications: International Joint Conference, VISIGRAPP 2010, Angers, France, May 17-21, 2010. Revised Selected Papers-
dc.description.abstractUnrestricted zoom paths are much desired to gain deep understandings during visual analysis of relational databases. We present a multiscale visualization system supporting unrestricted zoom paths. Our system has a flexible visual interface on the client side, called "ZoomTree", and a powerful and efficient back end with GPU-based parallel online data cubing and CPU-based data clustering. Zoom-trees are seamlessly integrated with a table-based overview using "hyperlinks" embedded in the table, and are designed to represent the entire history of a zooming process that reveals multiscale data characteristics. Arbitrary branching and backtracking in a zoom-tree are made possible by our fast parallel online cubing algorithm for partially materialized data cubes. Partial materialization provides a good tradeoff among preprocessing time, storage and online query time. Experiments and a user study have confirmed the effectiveness of our design. © 2011 Springer-Verlag.en_US
dc.languageengen_US
dc.relation.ispartofCommunications in Computer and Information Scienceen_US
dc.titleZoomTree: Unrestricted zoom paths in multiscale visual analysis of relational databasesen_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.doi10.1007/978-3-642-25382-9_21en_US
dc.identifier.scopuseid_2-s2.0-84863270196-
dc.identifier.hkuros200758-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84857584197&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume229 CCISen_US
dc.identifier.spage299en_US
dc.identifier.epage317en_US
dc.identifier.scopusauthoridWang, B=55035927000en_US
dc.identifier.scopusauthoridChen, G=55031902700en_US
dc.identifier.scopusauthoridBu, J=7005200782en_US
dc.identifier.scopusauthoridYu, Y=8554163500en_US

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