Intelligent Visual Analytics Queries

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HAO, Ming C., Umeshwar DAYAL, Daniel A. KEIM, Dominik MORENT, Jörn SCHNEIDEWIND, 2007. Intelligent Visual Analytics Queries. 2007 IEEE Symposium on Visual Analytics Science and Technology. Sacramento, CA, USA, Oct 30, 2007 - Nov 1, 2007. In: 2007 IEEE Symposium on Visual Analytics Science and Technology. IEEE, pp. 91-98. ISBN 978-1-4244-1659-2. Available under: doi: 10.1109/VAST.2007.4389001

@inproceedings{Hao2007-10Intel-5628, title={Intelligent Visual Analytics Queries}, year={2007}, doi={10.1109/VAST.2007.4389001}, isbn={978-1-4244-1659-2}, publisher={IEEE}, booktitle={2007 IEEE Symposium on Visual Analytics Science and Technology}, pages={91--98}, author={Hao, Ming C. and Dayal, Umeshwar and Keim, Daniel A. and Morent, Dominik and Schneidewind, Jörn} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dspace:hasBitstream rdf:resource=""/> <dc:creator>Keim, Daniel A.</dc:creator> <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights> <dc:contributor>Morent, Dominik</dc:contributor> <dc:creator>Schneidewind, Jörn</dc:creator> <bibo:uri rdf:resource=""/> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:contributor>Schneidewind, Jörn</dc:contributor> <dcterms:bibliographicCitation>First publ. in: IEEE Symposium on Visual Analytics and Technology (VAST 2007),Sacramento, CA, USA, October 30 - November 1, 2007</dcterms:bibliographicCitation> <dc:format>application/pdf</dc:format> <dcterms:hasPart rdf:resource=""/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:contributor>Keim, Daniel A.</dc:contributor> <dcterms:rights rdf:resource=""/> <dspace:isPartOfCollection rdf:resource=""/> <dc:contributor>Hao, Ming C.</dc:contributor> <dc:contributor>Dayal, Umeshwar</dc:contributor> <dc:creator>Morent, Dominik</dc:creator> <dc:date rdf:datatype="">2011-03-24T15:57:19Z</dc:date> <dcterms:isPartOf rdf:resource=""/> <dc:creator>Hao, Ming C.</dc:creator> <dc:creator>Dayal, Umeshwar</dc:creator> <dc:language>eng</dc:language> <dcterms:title>Intelligent Visual Analytics Queries</dcterms:title> <dcterms:available rdf:datatype="">2011-03-24T15:57:19Z</dcterms:available> <dcterms:abstract xml:lang="eng">Visualizations of large multi-dimensional data sets, occurring in scientific and commercial applications, often reveal interesting local patterns. Analysts want to identify the causes and impacts of these interesting areas, and they also want to search for similar patterns occurring elsewhere in the data set. In this paper we introduce the Intelligent Visual Analytics Query (IVQuery) concept that combines visual interaction with automated analytical methods to support analysts in discovering the special properties and relations of identified patterns. The idea of IVQuery is to interactively select focus areas in the visualization. Then, based on the characteristics of the selected areas, such as the selected data dimensions and data records, IVQuery employs analytical methods to identify the relationships to other portions of the data set. Finally, IVQuery generates visual representations for analysts to view and refine the results. IVQuery has been applied successfully to different real-world data sets, such as data warehouse performance, product sales, and sever performance analysis, and demonstrates the benefits of this technique over traditional filtering and zooming techniques. The visual analytics query technique can be used with many different types of visual representation. In this paper we show how to use IVQuery with parallel coordinates, visual maps, and scatter plots.</dcterms:abstract> <dcterms:issued>2007-10</dcterms:issued> </rdf:Description> </rdf:RDF>

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