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Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
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SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Adaptive, multiresolution visualization of large data sets using a distributed memory octree
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
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FRONTIERS '99 Proceedings of the The 7th Symposium on the Frontiers of Massively Parallel Computation
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SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
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IEEE Transactions on Visualization and Computer Graphics
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
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Proceedings of the 2006 ACM SIGMOD international conference on Management of data
FFT and Convolution Performance in Image Filtering on GPU
IV '06 Proceedings of the conference on Information Visualization
Data exploration of turbulence simulations using a database cluster
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
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Proceedings of the 18th ACM international symposium on High performance distributed computing
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Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
I/O streaming evaluation of batch queries for data-intensive computational turbulence
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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We present a query processing framework for the efficient evaluation of spatial filters on large numerical simulation datasets stored in a data-intensive cluster. Previously, filtering of large numerical simulations stored in scientific databases has been impractical owing to the immense data requirements. Rather, filtering is done during simulation or by loading snapshots into the aggregate memory of an HPC cluster. Our system performs filtering within the database and supports large filter widths. We present two complementary methods of execution: I/O streaming computes a batch filter query in a single sequential pass using incremental evaluation of decomposable kernels, summed volumes generates an intermediate data set and evaluates each filtered value by accessing only eight points in this dataset. We dynamically choose between these methods depending upon workload characteristics. The system allows us to perform filters against large data sets with little overhead: query performance scales with the cluster's aggregate I/O throughput.