東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Large scale data analytics

Cho, Chung Yik.

FindBook

Google Book

Amazon

博客來

Large scale data analytics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Large scale data analytics/ by Chung Yik Cho ... [et al.].
其他作者:	Cho, Chung Yik.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	ix, 89 p. :ill., digital ;24 cm.
內容註:	Introduction -- Background -- Large Scale Data Analytics -- Query Framework -- Results and Discussion -- Conclusion and Future Works.
Contained By:	Springer eBooks
標題:	Big data. -
電子資源:	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
LDR:02105nmm a2200337 a 4500 001 2178631
003 DE-He213
005 20190703140605.0
006 m d
007 cr nn 008maaau
008 191122s2019 gw s 0 eng d
020 $a 9783030038922 $q (electronic bk.)
020 $a 9783030038915 $q (paper)
024 7 $a 10.1007/978-3-030-03892-2 $2 doi
035 $a 978-3-030-03892-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45
072 7 $a TBJ $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a TBJ $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b L322 2019
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
773 0 $t Springer eBooks
830 0 $a Data, semantics and cloud computing ; $v v.806. $3 3383016
856 4 0 $u https://doi.org/10.1007/978-3-030-03892-2
950 $a Engineering (Springer-11647)