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. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Johnson, C. R., Glatter, M., Kendall, W., Huang, J., & Hoffman, F. (2009). Querying for feature extraction and visualization in climate modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5545 LNCS, pp. 416–425). https://doi.org/10.1007/978-3-642-01973-9_46
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