论文信息 - The parallel processing of spatial selection for very large geo-spatial databases

The parallel processing of spatial selection for very large geo-spatial databases

Earth science (ES) applications handle very large geo-spatial data sets and interactive response time is required by its query processing. Spatial selection is one of the very important basic operations for geo-spatial databases. It retrieves all the objects that intersect with a given point or rectangle. We present a novel approach for the parallel processing of spatial selection of very large geo-spatial databases using partitioned parallelism. To evaluate this approach, we use the Extended Sequoia 2000 benchmark, which has real world data and real queries. In addition, we use an actual object database management system, ShusseUo, which we developed previously. The experimental results of parallel processing of spatial selection show good speed-up.

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