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DEVise: integrated querying and visual exploration of large datasets
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Source International Conference on Management of Data archive
Proceedings of the 1997 ACM SIGMOD international conference on Management of data table of contents
Tucson, Arizona, United States
Pages: 301 - 312  
Year of Publication: 1997
ISBN:0-89791-911-4
Also published in ...
Authors
M. Livny  Department of Computer Sciences, University of Wisconsin-Madison
R. Ramakrishnan  Department of Computer Sciences, University of Wisconsin-Madison
K. Beyer  Department of Computer Sciences, University of Wisconsin-Madison
G. Chen  Department of Computer Sciences, University of Wisconsin-Madison
D. Donjerkovic  Department of Computer Sciences, University of Wisconsin-Madison
S. Lawande  Department of Computer Sciences, University of Wisconsin-Madison
J. Myllymaki  Department of Computer Sciences, University of Wisconsin-Madison
K. Wenger  Department of Computer Sciences, University of Wisconsin-Madison
Sponsor
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 14,   Downloads (12 Months): 59,   Citation Count: 30
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ABSTRACT

DEVise is a data exploration system that allows users to easily develop, browse, and share visual presentation of large tabular datasets (possibly containing or referencing multimedia objects) from several sources. The DEVise framework is being implemented in a tool that has been already successfully applied to a variety of real applications by a number of user groups. Our emphasis is on developing an intuitive yet powerful set of querying and visualization primitives that can be easily combined to develop a rich set of visual presentations that integrate data from a wide range of application domains. While DEVise is a powerful visualization tool, its greatest strengths are the ability to interactively explore a visual presentation of the data at any level of detail (including retrieving individual data records), and the ability to seamlessly query and combine data from a variety of local and remote sources. In this paper, we present the DEVise framework, describe the current tool, and report on our experience in applying it to several real applications.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Robert Braham. Math & visualization: New tools, new frontiers. IEEE Spectrum, 32(11):19-36, November 1995.
 
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Michael Cheat, Miron Livny, and Raghu Ramakrishnan. Visual analysis of stream data. In Proc. of SPIE- The International Society .for Optical Engineering, volume 2410, pages 108-119, San Jose, CA, April 1995.
 
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Miron Livny, Raghu Ramakrishnan, and Jussi Myllymaki. Visual exploration of large data sets. In Proc. of SPIE-- The International Society for Optical Engineering, volume 2657', San Jose, CA, January 1996.
 
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Eric Petajan, Yves Jean, Dan Lieuwen, and Vinod Anupam. Data.Space: An Automated Visualization System for Large Databases. In Proceedings of SPIE, Visual Data Exploration and Analysis IV, volume 3017. The International Society for Optical Engineering, 1997.
 
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The Khoros Group. Khoros Manual. University of New Mexico, Albuquerque, NM 87131, 1991.
 
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CITED BY  30
 
 
 
 
 
 
 
 
 
 
 

Collaborative Colleagues:
M. Livny: colleagues
R. Ramakrishnan: colleagues
K. Beyer: colleagues
G. Chen: colleagues
D. Donjerkovic: colleagues
S. Lawande: colleagues
J. Myllymaki: colleagues
K. Wenger: colleagues

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