Multidimensional access methods
ACM Computing Surveys (CSUR)
Quadtree and R-tree indexes in oracle spatial: a comparison using GIS data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
Spatial indexing in microsoft SQL server 2008
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Designing efficient sorting algorithms for manycore GPUs
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Analyses of multi-level and multi-component compressed bitmap indexes
ACM Transactions on Database Systems (TODS)
HFPaC: GPU friendly height field parallel compression
Geoinformatica
CudaGIS: report on the design and realization of a massive data parallel GIS on GPUs
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
Parallel spatial query processing on GPUs using R-trees
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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Global remote sensing and large-scale environmental modeling have generated huge amounts of raster geospatial data. While the inherent data parallelism of large-scale raster geospatial data allows straightforward coarse-grained parallelization at the chunk level on CPUs, it is largely unclear how to effectively exploit such data parallelism on massively parallel General Purpose Graphics Processing Units (GPGPUs) that require fine-grained parallelization. In this study, we have developed an efficient spatial data structure called BQ-Tree to code raster geospatial data by exploiting the uniform distributions of quadrants of bitmaps at the bitplanes of a raster. A fine-grained parallelization scheme has been implemented using Nvidia CUDA. Experiments show that the GPGPU implementation is capable of decoding a BQ-Tree encoded 16-bits NASA MODIS geospatial raster with 22,658*15,586 cells in 190 milliseconds, i.e., 1.86 billion cells per second, on an Nvidia C2050 GPU card. The performance achieves a 5.9X speedup when compared with the best dual quadcore CPU implementation and a 36.9X speedup compared with a highly optimized single core CPU implementation.