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2017 | OriginalPaper | Chapter

An Efficient Encoding Scheme for Dynamic Multidimensional Datasets

Authors : Mehnuma Tabassum Omar, K. M. Azharul Hasan

Published in: Pattern Recognition and Machine Intelligence

Publisher: Springer International Publishing

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Abstract

Big Data involve composite, undefined volume and unspecified rate of datasets [1]. The index array lags behind the conventional approaches to maintain the data velocity by allowing subjective expansion on the boundary of array dimension. The major concern of large volume applications like “Big Data” is to perceive data volume and high velocity for further operations. In this paper we offer a scalable encoding scheme that replaces data block allocation with segment allocation and reorganizes the n dimensions of array into 2 dimensions only. Hence it requires 2 indices for data encoding and offers low indexing cost.

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Title: An Efficient Encoding Scheme for Dynamic Multidimensional Datasets
Authors: Mehnuma Tabassum Omar
K. M. Azharul Hasan
Publisher: Springer International Publishing
Book: Pattern Recognition and Machine Intelligence
Print ISBN: 978-3-319-69899-1

Electronic ISBN: 978-3-319-69900-4

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-69900-4_66

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