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

Big data 2.0 processing systems = a ...

Sakr, Sherif.

FindBook

Google Book

Amazon

博客來

Big data 2.0 processing systems = a systems overview /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Big data 2.0 processing systems/ by Sherif Sakr.
其他題名:	a systems overview /
作者:	Sakr, Sherif.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xvi, 145 p. :ill., digital ;24 cm.
內容註:	Introduction -- General-Purpose Big Data Processing Systems -- Large-Scale Processing Systems of Structured Data -- Large-Scale Graph Processing Systems -- Large-Scale Stream Processing Systems -- Large-Scale Machine/Deep Learning Frameworks -- Conclusions and Outlook.
Contained By:	Springer Nature eBook
標題:	Big data. -
電子資源:	https://doi.org/10.1007/978-3-030-44187-6
ISBN:	9783030441876

Big data 2.0 processing systems = a systems overview /
Sakr, Sherif.

Big data 2.0 processing systemsa systems overview /[electronic resource] :by Sherif Sakr. - Second edition. - Cham :Springer International Publishing :2020. - xvi, 145 p. :ill., digital ;24 cm.

Introduction -- General-Purpose Big Data Processing Systems -- Large-Scale Processing Systems of Structured Data -- Large-Scale Graph Processing Systems -- Large-Scale Stream Processing Systems -- Large-Scale Machine/Deep Learning Frameworks -- Conclusions and Outlook.

This book provides readers the "big picture" and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data) The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Next, Chapter 6 focuses on covering the emerging frameworks and systems in the domain of scalable machine learning and deep learning processing. Lastly, Chapter 7 shares conclusions and an outlook on future research challenges. This new and considerably enlarged second edition not only contains the completely new chapter 6, but also offers a refreshed content for the state-of-the-art in all domains of big data processing over the last years. Overall, the book offers a valuable reference guide for professional, students, and researchers in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.

ISBN: 9783030441876

Standard No.: 10.1007/978-3-030-44187-6doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Big data 2.0 processing systems = a systems overview /
LDR:03484nmm a2200349 a 4500 001 2222342
003 DE-He213
005 20200710160719.0
006 m d
007 cr nn 008maaau
008 210108s2020 sz s 0 eng d
020 $a 9783030441876 $q (electronic bk.)
020 $a 9783030441869 $q (paper)
024 7 $a 10.1007/978-3-030-44187-6 $2 doi
035 $a 978-3-030-44187-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45
072 7 $a UNH $2 bicssc
072 7 $a COM030000 $2 bisacsh
072 7 $a UNH $2 thema
072 7 $a UND $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b S158 2020
100 1 $a Sakr, Sherif. $3 2209883
245 1 0 $a Big data 2.0 processing systems $h [electronic resource] : $b a systems overview / $c by Sherif Sakr.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xvi, 145 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- General-Purpose Big Data Processing Systems -- Large-Scale Processing Systems of Structured Data -- Large-Scale Graph Processing Systems -- Large-Scale Stream Processing Systems -- Large-Scale Machine/Deep Learning Frameworks -- Conclusions and Outlook.
520 $a This book provides readers the "big picture" and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data) The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Next, Chapter 6 focuses on covering the emerging frameworks and systems in the domain of scalable machine learning and deep learning processing. Lastly, Chapter 7 shares conclusions and an outlook on future research challenges. This new and considerably enlarged second edition not only contains the completely new chapter 6, but also offers a refreshed content for the state-of-the-art in all domains of big data processing over the last years. Overall, the book offers a valuable reference guide for professional, students, and researchers in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.
650 0 $a Big data. $3 2045508
650 0 $a Databases. $3 747532
650 1 4 $a Information Storage and Retrieval. $3 761906
650 2 4 $a IT in Business. $3 2114922
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Database Management. $3 891010
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-44187-6
950 $a Computer Science (SpringerNature-11645)