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Scalable Distributed Storage for Big Scientific Data
ذخیره سازی توزیع شده مقیاس پذیر برای داده های علمی بزرگ-2018 In this paper we describe the distributed storage
structure with indexing techniques which can be applied in
scientific Big Data centers. Basic principal of this project is
distributed (N,K)-block storage. We develop the descent of
LH*RS especially for multidimensional data arrays using
multiscaled representation of these arrays and efficient preprocessing algorithms. Dataset is decomposed into data blocks of
several levels using the Wavelet transform. The required dataset
of any requested scale and resolution is reconstructed online
from the corresponding set of downloaded blocks on client’s side.
In order to accelerate data queries processing we can additionally
use a pre-computed statistic results blocks and their hierarchical
representation. The main advantage of this approach is that we
can use these results together with original data or even
separately to serve different data queries with both value and
dimension subsetting conditions. This approach can reduce the
resource costs of corresponding scientific problems.
Keywords: distributed storage schema; wavelet transform;Hilbert space-filling curve; indexing; multidimensional array |
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