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Published Articles >> Table of Contents >> Abstract
18th International Conference on Scientific and Statistical Database Management (SSDBM'06)
pp. 179-183
Efficient Storage Allocation of Large-Scale Extendible Multi-dimensional Scientific Datasets
Ekow J. Otoo, Lawrence Berkeley National Laboratory, UC Berkeley
Doron Rotem, Lawrence Berkeley National Laboratory, UC Berkeley
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SSDBM.2006.24
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| Abstract |
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Data analyses in scientific domains involve storage, retrieval,
processing and visualization of large scale multidimensional
datasets. The datasets incrementally grow by
appending new data to the dataset without reorganizing the
already allocated data storage. The datasets, typically modeled
as k-dimensional arrays, are maintained in files where
the array elements are allocated in a sequence of consecutive
storage locations according to some ordering of an array
mapping function. Such mapping functions limit the degree
of extendibility of the arrays to one dimension only. To
allowing new data elements to be appended to the dataset
effectively implies allowing for the arbitrary extendibility of
the array. We present a mapping function F.(), that uses
additional O(k log E ) storage, where E is the number of
array segments appended. The function is realized with
approximately the approximately the same order of complexity
as a conventional array function. The algorithm
presented for addressing elements of datasets in extendible
multi-dimensional array files apply equally well to addressing
memory resident extendible arrays.
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 Additional Information
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Index Terms- Multi-dimensional array, scientific
dataset, extendible array, computed array addressing
function, out-of-core array storage.
Citation:
Ekow J. Otoo, Doron Rotem,
"Efficient Storage Allocation of Large-Scale Extendible Multi-dimensional Scientific Datasets,"
ssdbm,
pp. 179-183,
18th International Conference on Scientific and Statistical Database Management (SSDBM'06),
2006
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