國立虎尾科技大學 |

Systems for big graph analytics

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Systems for big graph analytics/ by Da Yan, Yuanyuan Tian, James Cheng.
Author:	Yan, Da.
other author:	Tian, Yuanyuan.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	vi, 92 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Graph algorithms. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-58217-7
ISBN:	9783319582177

Systems for big graph analytics
Yan, Da.

Systems for big graph analytics[electronic resource] /by Da Yan, Yuanyuan Tian, James Cheng. - Cham :Springer International Publishing :2017. - vi, 92 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

1 Introduction -- 2 Pregel-Like Systems -- 3 Hands-On Experiences -- 4 Shared Memory Abstraction -- 5 Block-Centric Computation -- 6 Subgraph-Centric Graph Mining -- 7 Matrix-Based Graph Systems -- 8 Conclusions.

There has been a surging interest in developing systems for analyzing big graphs generated by real applications, such as online social networks and knowledge graphs. This book aims to help readers get familiar with the computation models of various graph processing systems with minimal time investment. This book is organized into three parts, addressing three popular computation models for big graph analytics: think-like-a-vertex, think-likea- graph, and think-like-a-matrix. While vertex-centric systems have gained great popularity, the latter two models are currently being actively studied to solve graph problems that cannot be efficiently solved in vertex-centric model, and are the promising next-generation models for big graph analytics. For each part, the authors introduce the state-of-the-art systems, emphasizing on both their technical novelties and hands-on experiences of using them. The systems introduced include Giraph, Pregel+, Blogel, GraphLab, CraphChi, X-Stream, Quegel, SystemML, etc. Readers will learn how to design graph algorithms in various graph analytics systems, and how to choose the most appropriate system for a particular application at hand. The target audience for this book include beginners who are interested in using a big graph analytics system, and students, researchers and practitioners who would like to build their own graph analytics systems with new features.

ISBN: 9783319582177

Standard No.: 10.1007/978-3-319-58217-7doiSubjects--Topical Terms:

713780
Graph algorithms.

LC Class. No.: QA166.245

Dewey Class. No.: 518.1

Systems for big graph analytics
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245 1 0 $a Systems for big graph analytics $h [electronic resource] / $c by Da Yan, Yuanyuan Tian, James Cheng.
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505 0 $a 1 Introduction -- 2 Pregel-Like Systems -- 3 Hands-On Experiences -- 4 Shared Memory Abstraction -- 5 Block-Centric Computation -- 6 Subgraph-Centric Graph Mining -- 7 Matrix-Based Graph Systems -- 8 Conclusions.
520 $a There has been a surging interest in developing systems for analyzing big graphs generated by real applications, such as online social networks and knowledge graphs. This book aims to help readers get familiar with the computation models of various graph processing systems with minimal time investment. This book is organized into three parts, addressing three popular computation models for big graph analytics: think-like-a-vertex, think-likea- graph, and think-like-a-matrix. While vertex-centric systems have gained great popularity, the latter two models are currently being actively studied to solve graph problems that cannot be efficiently solved in vertex-centric model, and are the promising next-generation models for big graph analytics. For each part, the authors introduce the state-of-the-art systems, emphasizing on both their technical novelties and hands-on experiences of using them. The systems introduced include Giraph, Pregel+, Blogel, GraphLab, CraphChi, X-Stream, Quegel, SystemML, etc. Readers will learn how to design graph algorithms in various graph analytics systems, and how to choose the most appropriate system for a particular application at hand. The target audience for this book include beginners who are interested in using a big graph analytics system, and students, researchers and practitioners who would like to build their own graph analytics systems with new features.
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