Abstract
We present a study to show the possibility of using two well-known space partitioning and indexing techniques, kd trees and quad trees, in declustering applications to increase input/output (I/O) parallelization and reduce spatial data processing times. This parallelization enables time-consuming computational geometry algorithms to be applied efficiently to big spatial data rendering and querying. The key challenge is how to balance the spatial processing load across a large number of worker nodes, given significant performance heterogeneity in nodes and processing skews in the workload.
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ORCID: Ahmet SAYAR, http://orcid.org/0000-0002-6335-459X; Süleyman EKEN, http://orcid.org/0000-0001-9488-908X
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Sayar, A., Eken, S. & Öztürk, O. Kd-tree and quad-tree decompositions for declustering of 2D range queries over uncertain space. Frontiers Inf Technol Electronic Eng 16, 98–108 (2015). https://doi.org/10.1631/FITEE.1400165
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DOI: https://doi.org/10.1631/FITEE.1400165