000			03775nam a22003855i 4500
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008			160824s2016 gw | s |||| 0|eng d
020			_a9783319387765
040			_aES-MaUEC
050		4	_aQA76.9.B45 _bS257 2016
082	0	4	_a005.74
100	1		_aSakr, Sherif, _d1979- _0Local _997763
245	1	0	_aBig Data 2.0 Processing Systems : _bA Survey _cby Sherif Sakr
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XV, 102 páginas) _b49 ilustraciones
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aSpringerBriefs in Computer Science _x2191-5768
505	0		_aChapter 1: Introduction -- Chapter 2: General Purpose Big Data Processing Systems -- Chapter 3: Large Scale Processing of Structured Databases -- Chapter 4: Large Scale Graph Processing Systems -- Chapter 5: Large Scale Stream Processing Systems -- Chapter 6: Conclusions and Outlook. .
520	3		_aThis book provides readers the zbig picturey 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 big data processing scenarios such as the large-scale processing of structured data, graph data and streaming data. 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. Lastly, Chapter 6 shares conclusions and an outlook on future research challenges. Overall, the book offers a valuable reference guide for students, researchers and professionals 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	7	_aData mining _0 _2embne _9162648
650	0	7	_aSistemas de gestión de bases de datos _0 _2embne _9150569
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-38776-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319387765
907			_a.b12953684 _b10-10-17 _c21-11-16
942			_2lcc _cLE
945			_aQA76.9.B45 S257 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11596156 _z06-04-17
988			_aEBOOK, asignarmaterias , EBSPRINGER
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0