Publikation:

Intelligent Visual Analytics Queries

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VisQuery_073107_FinalSubmit.pdf
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Datum

2007

Autor:innen

Hao, Ming C.
Dayal, Umeshwar
Schneidewind, Jörn

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-68820
DOI (zitierfähiger Link)
https://doi.org/10.1109/VAST.2007.4389001
ArXiv-ID

Internationale Patentnummer

Link zur Lizenz
Attribution-NonCommercial-NoDerivs 2.0 Generic

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Green

Sammlungen

Informatik und Informationswissenschaft: Publikationen
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Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

2007 IEEE Symposium on Visual Analytics Science and Technology. IEEE, 2007, pp. 91-98. ISBN 978-1-4244-1659-2. Available under: doi: 10.1109/VAST.2007.4389001

Zusammenfassung

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.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Visual Analytics Query, Similarity Queries, Interactive Queries

Konferenz

2007 IEEE Symposium on Visual Analytics Science and Technology, 30. Okt. 2007 - 1. Nov. 2007, Sacramento, CA, USA
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ISO 690HAO, 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, 30. Okt. 2007 - 1. Nov. 2007. In: 2007 IEEE Symposium on Visual Analytics Science and Technology. IEEE, 2007, pp. 91-98. ISBN 978-1-4244-1659-2. Available under: doi: 10.1109/VAST.2007.4389001
BibTex
@inproceedings{Hao2007-10Intel-5628,
  year={2007},
  doi={10.1109/VAST.2007.4389001},
  title={Intelligent Visual Analytics Queries},
  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}
}
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Interner Vermerk

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