@InProceedings{Johnson_LNCS_20090527,
 author		= {C. Ryan Johnson and Markus Glatter and Wesley Kendall and Jian Huang and Forrest M. Hoffman},
 title		= {Querying for Feature Extraction and Visualization in Climate Modeling},
 booktitle	= {Proceedings of the 9th International Conference on Computational Science ({ICCS 2009})},
 editor		= {Gabrielle Allen and Jaros{\l}aw Nabrzyski and Edward Seidel and Geert Dick van Albada and Jack Dongarra and Peter M.A. Sloot},
 publisher	= {Springer-Verlag},
 address	= {Heidelberg},
 series		= {Lecture Notes in Computer Science ({LNCS})},
 dates		= {25--27 May 2009},
 location	= {Baton Rogue, Louisiana, USA},
 volume		= 5545,
 pages		= {416--425},
 doi		= {10.1007/978-3-642-01973-9\_46},
 isbn		= {978-3-642-01972-2},
 day		= 27,
 month		= may,
 year		= 2009,
 abstract	= {The ultimate goal of data visualization is to clearly portray features relevant to the problem being studied. This goal can be realized only if users can effectively communicate to the visualization software what features are of interest. To this end, we describe in this paper two query languages used by scientists to locate and visually emphasize relevant data in both space and time. These languages offer descriptive feedback and interactive refinement of query parameters, which are essential in any framework supporting queries of arbitrary complexity. We apply these languages to extract features of interest from climate model results and describe how they support rapid feature extraction from large datasets.}
}