000			03030nam a22003735i 4500
001			387254
003			ES-MaUEC
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007			cr nn 008mamaa
008			220601s2010 sz | s |||| 0|eng d
020			_a9783031018374
024	7		_a10.1007/978-3-031-01837-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
050		4	_aQA79.6.D3 _b2010
100	1		_aGolab, Lukasz _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aData Stream Management _cby Lukasz Golab, M. Tamer Ozsu.
250			_a1st edition 2010
264		1	_aCham _bSpringer International Publishing _c2010
300			_a1 recurso en línea (VIII, 65 páginas) _b
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Management _x2153-5426
505	0		_aIntroduction -- Data Stream Management Systems -- Streaming Data Warehouses -- Conclusions.
520			_aMany applications process high volumes of streaming data, among them Internet traffic analysis, financial tickers, and transaction log mining. In general, a data stream is an unbounded data set that is produced incrementally over time, rather than being available in full before its processing begins. In this lecture, we give an overview of recent research in stream processing, ranging from answering simple queries on high-speed streams to loading real-time data feeds into a streaming warehouse for off-line analysis. We will discuss two types of systems for end-to-end stream processing: Data Stream Management Systems (DSMSs) and Streaming Data Warehouses (SDWs). A traditional database management system typically processes a stream of ad-hoc queries over relatively static data. In contrast, a DSMS evaluates static (long-running) queries on streaming data, making a single pass over the data and using limited working memory. In the first part of this lecture, we will discuss research problems in DSMSs, such as continuous query languages, non-blocking query operators that continually react to new data, and continuous query optimization. The second part covers SDWs, which combine the real-time response of a DSMS by loading new data as soon as they arrive with a data warehouse's ability to manage Terabytes of historical data on secondary storage. Table of Contents: Introduction / Data Stream Management Systems / Streaming Data Warehouses / Conclusions.
700	1		_aOzsu, M. Tamer _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031007095
776	0	8	_iPrinted edition: _z9783031029653
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01837-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSynthesis Collection of Technology_2010
999			_c387254 _d387254