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Large scale data analytics

Cho, Chung Yik.

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Large scale data analytics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Large scale data analytics/ by Chung Yik Cho ... [et al.].
other author:	Cho, Chung Yik.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	ix, 89 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Background -- Large Scale Data Analytics -- Query Framework -- Results and Discussion -- Conclusion and Future Works.
Contained By:	Springer eBooks
Subject:	Big data. -
Online resource:	https://doi.org/10.1007/978-3-030-03892-2
ISBN:	9783030038922

Large scale data analytics
Large scale data analytics[electronic resource] /by Chung Yik Cho ... [et al.]. - Cham :Springer International Publishing :2019. - ix, 89 p. :ill., digital ;24 cm. - Data, semantics and cloud computing,v.8062524-6593 ;. - Data, semantics and cloud computing ;v.806..

Introduction -- Background -- Large Scale Data Analytics -- Query Framework -- Results and Discussion -- Conclusion and Future Works.

This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness.

ISBN: 9783030038922

Standard No.: 10.1007/978-3-030-03892-2doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Large scale data analytics
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245 0 0 $a Large scale data analytics $h [electronic resource] / $c by Chung Yik Cho ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a ix, 89 p. : $b ill., digital ; $c 24 cm.
490 1 $a Data, semantics and cloud computing, $x 2524-6593 ; $v v.806
505 0 $a Introduction -- Background -- Large Scale Data Analytics -- Query Framework -- Results and Discussion -- Conclusion and Future Works.
520 $a This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness.
650 0 $a Big data. $3 2045508
650 1 4 $a Mathematical and Computational Engineering. $3 3226306
700 1 $a Cho, Chung Yik. $3 3383015
710 2 $a SpringerLink (Online service) $3 836513
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830 0 $a Data, semantics and cloud computing ; $v v.806. $3 3383016
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