000			04125nam a22003735i 4500
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003			ES-MaUEC
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008			170106s2016 gw | s |||| 0|eng d
020			_a9783319474311
024	7		_a10.1007/978-3-319-47431-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D35 _b2016 EB
100	1		_aSakr, Sherif, _997763 _d1979-
245	1	0	_aLarge-Scale Graph Processing Using Apache Giraph _cby Sherif Sakr, Faisal Moeen Orakzai, Ibrahim Abdelaziz, Zuhair Khayyat
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XXV, 197 páginas) _b102 ilustraciones, 87 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_a1. Introduction -- 2. Getting started with Giraph -- 3. Giraph-In-Action: Implementing Popular Graph Algorithms using Giraph -- 4. Giraph Programming Optimizations: Tips and Tricks -- 5. Similar Systems to Giraph -- 6. Conclusions.
520			_aThis book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms. The book is organized as follows: Chapter 1 starts by providing a general background of the big data phenomenon and a general introduction to the Apache Giraph system, its abstraction, programming model and design architecture. Next, chapter 2 focuses on Giraph as a platform and how to use it. Based on a sample job, even more advanced topics like monitoring the Giraph application lifecycle and different methods for monitoring Giraph jobs are explained. Chapter 3 then provides an introduction to Giraph programming, introduces the basic Giraph graph model and explains how to write Giraph programs. In turn, Chapter 4 discusses in detail the implementation of some popular graph algorithms including PageRank, connected components, shortest paths and triangle closing. Chapter 5 focuses on advanced Giraph programming, discussing common Giraph algorithmic optimizations, tunable Giraph configurations that determine the system's utilization of the underlying resources, and how to write a custom graph input and output format. Lastly, chapter 6 highlights two systems that have been introduced to tackle the challenge of large scale graph processing, GraphX and GraphLab, and explains the main commonalities and differences between these systems and Apache Giraph. This book serves as an essential reference guide for students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples and the complete source code available in the related github repository. Students will find a comprehensive introduction to and hands-on practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage of the emerging and ongoing advancements in big graph processing systems.
988			_aEBOOK, EBSPRINGER
650	0	7	_aEstructuras de datos (Informática) _2embne _9151535
650	0	7	_aSistemas de gestión de bases de datos _2embne _9150569
700	1		_aOrakzai, Faisal Moeen. _9101781 _0Local _eautor
700	1		_aAbdelaziz, Ibrahim _0Local _eautor _9101782
700	1		_aKhayyat, Zuhair _0Local _eautor _9101783
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-47431-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2021 _dz _eu _feng _ggw _h0