Systems for Big Graph Analytics

• Κύριοι συγγραφείς: Yan, Da (Συγγραφέας), Tian, Yuanyuan (Συγγραφέας), Cheng, James (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: SpringerBriefs in Computer Science,
• Θέματα: Computer science.; Computer communication systems.; Computers.; Computer graphics.; Computer Science.; Information Systems and Communication Service.; Computer Graphics.; Computer Communication Networks.

 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. 

 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.