Querying for Feature Extraction and Visualization in Climate Modeling
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Terascale data organization for discovering multivariate climatic trends
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
In situ visualization at extreme scale: challenges and opportunities
IEEE Computer Graphics and Applications
A study of hierarchical correlation clustering for scientific volume data
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
An analytical framework for particle and volume data of large-scale combustion simulations
UltraVis '13 Proceedings of the 8th International Workshop on Ultrascale Visualization
A classification of scientific visualization algorithms for massive threading
UltraVis '13 Proceedings of the 8th International Workshop on Ultrascale Visualization
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Extracting and visualizing temporal patterns in large scientific data is an open problem in visualization research. First, there are few proven methods to flexibly and concisely define general temporal patterns for visualization. Second, with large time-dependent data sets, as typical with today’s large-scale simulations, scalable and general solutions for handling the data are still not widely available. In this work, we have developed a textual pattern matching approach for specifying and identifying general temporal patterns. Besides defining the formalism of the language, we also provide a working implementation with sufficient efficiency and scalability to handle large data sets. Using recent large-scale simulation data from multiple application domains, we demonstrate that our visualization approach is one of the first to empower a concept driven exploration of large-scale time-varying multivariate data.