| 書目名稱 | Systems for Big Graph Analytics | | 編輯 | Da Yan,Yuanyuan Tian,James Cheng | | 視頻video | http://file.papertrans.cn/885/884967/884967.mp4 | | 叢書名稱 | SpringerBriefs in Computer Science | | 圖書封面 |  | | 描述 | .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 aud | | 出版日期 | Book 2017 | | 關(guān)鍵詞 | Big graph analytics; Big data; Vertex-centric; Graph-centric; Matrix; Graph; System; Block-centric; Pregel; G | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-58217-7 | | isbn_softcover | 978-3-319-58216-0 | | isbn_ebook | 978-3-319-58217-7Series ISSN 2191-5768 Series E-ISSN 2191-5776 | | issn_series | 2191-5768 | | copyright | The Author(s) 2017 |
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