| 000 | 03438nam a22005055i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-01837-4 | ||
| 003 | DE-He213 | ||
| 005 | 20240730163728.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2010 sz | s |||| 0|eng d | ||
| 020 |
_a9783031018374 _9978-3-031-01837-4 |
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| 024 | 7 |
_a10.1007/978-3-031-01837-4 _2doi |
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| 050 | 4 | _aTK5105.5-5105.9 | |
| 072 | 7 |
_aUKN _2bicssc |
|
| 072 | 7 |
_aCOM043000 _2bisacsh |
|
| 072 | 7 |
_aUKN _2thema |
|
| 082 | 0 | 4 |
_a004.6 _223 |
| 100 | 1 |
_aGolab, Lukasz. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980230 |
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| 245 | 1 | 0 |
_aData Stream Management _h[electronic resource] / _cby Lukasz Golab, M. Tamer Ozsu. |
| 250 | _a1st ed. 2010. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2010. |
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| 300 |
_aVIII, 65 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aSynthesis Lectures on Data Management, _x2153-5426 |
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| 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. | ||
| 650 | 0 |
_aComputer networks . _931572 |
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| 650 | 0 |
_aData structures (Computer science). _98188 |
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| 650 | 0 |
_aInformation theory. _914256 |
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| 650 | 1 | 4 |
_aComputer Communication Networks. _980231 |
| 650 | 2 | 4 |
_aData Structures and Information Theory. _931923 |
| 700 | 1 |
_aOzsu, M. Tamer. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980232 |
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| 710 | 2 |
_aSpringerLink (Online service) _980233 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031007095 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031029653 |
| 830 | 0 |
_aSynthesis Lectures on Data Management, _x2153-5426 _980234 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01837-4 |
| 912 | _aZDB-2-SXSC | ||
| 942 | _cEBK | ||
| 999 |
_c84922 _d84922 |
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