Visualization Databases for the Analysis of Large Complex Datasets

Saptarshi Guha, Paul Kidwell, Ryan P. Hafen, William S. Cleveland
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:193-200, 2009.

Abstract

Comprehensive visualization that preserves the information in a large complex dataset requires a visualization database (VDB): many displays, some with many pages, and with one or more panels per page. A single display using a specific display method results from partitioning the data into subsets, sampling the subsets, and applying the method to each sample, typically one per panel. The time of the analyst to generate a display is not increased by choosing a large sample over a small one. Displays and display viewers can be designed to allow rapid scanning. Often, it is not necessary to view every page of a display. VDBs, already successful just with off-the-shelf tools, can be greatly improved by research that rethinks all of the areas of data visualization in the context of VDBs.

Cite this Paper


BibTeX
@InProceedings{pmlr-v5-guha09a, title = {Visualization Databases for the Analysis of Large Complex Datasets}, author = {Saptarshi Guha and Paul Kidwell and Ryan P. Hafen and William S. Cleveland}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {193--200}, year = {2009}, editor = {David van Dyk and Max Welling}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/guha09a/guha09a.pdf}, url = {http://proceedings.mlr.press/v5/guha09a.html}, abstract = {Comprehensive visualization that preserves the information in a large complex dataset requires a visualization database (VDB): many displays, some with many pages, and with one or more panels per page. A single display using a specific display method results from partitioning the data into subsets, sampling the subsets, and applying the method to each sample, typically one per panel. The time of the analyst to generate a display is not increased by choosing a large sample over a small one. Displays and display viewers can be designed to allow rapid scanning. Often, it is not necessary to view every page of a display. VDBs, already successful just with off-the-shelf tools, can be greatly improved by research that rethinks all of the areas of data visualization in the context of VDBs.} }
Endnote
%0 Conference Paper %T Visualization Databases for the Analysis of Large Complex Datasets %A Saptarshi Guha %A Paul Kidwell %A Ryan P. Hafen %A William S. Cleveland %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-guha09a %I PMLR %J Proceedings of Machine Learning Research %P 193--200 %U http://proceedings.mlr.press %V 5 %W PMLR %X Comprehensive visualization that preserves the information in a large complex dataset requires a visualization database (VDB): many displays, some with many pages, and with one or more panels per page. A single display using a specific display method results from partitioning the data into subsets, sampling the subsets, and applying the method to each sample, typically one per panel. The time of the analyst to generate a display is not increased by choosing a large sample over a small one. Displays and display viewers can be designed to allow rapid scanning. Often, it is not necessary to view every page of a display. VDBs, already successful just with off-the-shelf tools, can be greatly improved by research that rethinks all of the areas of data visualization in the context of VDBs.
RIS
TY - CPAPER TI - Visualization Databases for the Analysis of Large Complex Datasets AU - Saptarshi Guha AU - Paul Kidwell AU - Ryan P. Hafen AU - William S. Cleveland BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics PY - 2009/04/15 DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-guha09a PB - PMLR SP - 193 DP - PMLR EP - 200 L1 - http://proceedings.mlr.press/v5/guha09a/guha09a.pdf UR - http://proceedings.mlr.press/v5/guha09a.html AB - Comprehensive visualization that preserves the information in a large complex dataset requires a visualization database (VDB): many displays, some with many pages, and with one or more panels per page. A single display using a specific display method results from partitioning the data into subsets, sampling the subsets, and applying the method to each sample, typically one per panel. The time of the analyst to generate a display is not increased by choosing a large sample over a small one. Displays and display viewers can be designed to allow rapid scanning. Often, it is not necessary to view every page of a display. VDBs, already successful just with off-the-shelf tools, can be greatly improved by research that rethinks all of the areas of data visualization in the context of VDBs. ER -
APA
Guha, S., Kidwell, P., Hafen, R.P. & Cleveland, W.S.. (2009). Visualization Databases for the Analysis of Large Complex Datasets. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in PMLR 5:193-200

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