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020			_a9783540286080
040			_aES-MaUEC
050		4	_aQA76.9.D3 _bD383 2016
082	0	4	_a005.74
245	1	0	_aData Stream Management : _bProcessing High-Speed Data Streams _cedited by Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi
260			_aBerlin, Heidelberg _bSpringer Berlin Heidelberg _c2016
300			_a1 recurso en línea (VII, 537 páginas) _b103 ilustraciones, 16 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aData-Centric Systems and Applications _x2197-9723
505	0		_aPart I: Introduction -- Part II: Computation of Basic Stream Synopses -- Part III: Mining Data Streams -- Part IV: Advanced Topics -- Part V: Systems and Architectures -- Part VI: Applications. .
520	3		_aWe live in the era of zBig Datay: Petabytes of digital information are generated daily, and need to be processed and analyzed for interesting patterns and trends. Besides volume, a defining characteristic of Big Data is its velocity; that is, data is instantiated in the form of continuous, high-speed data streams that arrive at rapid rates, and need to be processed and analyzed on a continuous (24x7) basis. Such data streams pose very difficult challenges for conventional data-management architectures, which are built primarily on the concept of persistent, static data collections. This volume focuses on the theory and practice of data stream management, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains. A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets). Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics). Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud computing, big data analytics, and complex event processing), and a discussion of future directions in this exciting field. The book provides a comprehensive overview of core concepts and technological foundations, as well as various systems and applications, and is of particular interest to students, lecturers and researchers in the area of data stream management. .
650	0	7	_aData mining _2embne _9162648
650		7	_aEstructuras de datos (Informática) _2embne _9151535
700	1		_aGarofalakis, Minos _eeditor literario _0Local _9100067
700	1		_aGehrke, Johannes. _eeditor literario _940567 _0comprobar BNE20071184745
700	1		_aRastogi, Rajeev. _eeditor literario _9100068 _0Local
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-540-28608-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783540286080
907			_a.b12956600 _b10-10-17 _c21-11-16
942			_2lcc _cLE
945			_aQA76.9.D3 D383 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i1159844x _z06-04-17
988			_aEBOOK, asignarmaterias , EBSPRINGER
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0