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SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
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ACM Computing Surveys (CSUR)
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High Performance Cluster Computing: Architectures and Systems
High Performance Cluster Computing: Architectures and Systems
IEEE Transactions on Parallel and Distributed Systems
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CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
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SMI '04 Proceedings of the Shape Modeling International 2004
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Information Sciences—Informatics and Computer Science: An International Journal
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Parallel Computing - Heterogeneous computing
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Journal of Parallel and Distributed Computing
Evaluating error associated with lidar-derived DEM interpolation
Computers & Geosciences
A three tier architecture for LiDAR interpolation and analysis
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
LiDAR data management pipeline; from spatial database population to web-application visualization
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Octree-based indexing for 3D pointclouds within an Oracle Spatial DBMS
Computers & Geosciences
Process virtualization of large-scale lidar data in a cloud computing environment
Computers & Geosciences
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This paper proposes a novel method for distributed data organization and parallel data retrieval from huge volume point clouds generated by airborne Light Detection and Ranging (LiDAR) technology under a cluster computing environment, in order to allow fast analysis, processing, and visualization of the point clouds within a given area. The proposed method is suitable for both grid and quadtree data structures. As for distribution strategy, cross distribution of the dataset would be more efficient than serial distribution in terms of non-redundant datasets, since a dataset is more uniformly distributed in the former arrangement. However, redundant datasets are necessary in order to meet the frequent need of input and output operations in multi-client scenarios: the first copy would be distributed by a cross distribution strategy while the second (and later) would be distributed by an iterated exchanging distribution strategy. Such a distribution strategy would distribute datasets more uniformly to each data server. In data retrieval, a greedy algorithm is used to allocate the query task to a data server, where the computing load is lightest if the data block needing to be retrieved is stored among multiple data servers. Experiments show that the method proposed in this paper can satisfy the demands of frequent and fast data query.