| 000 | 03630nam a22005055i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-01846-6 | ||
| 003 | DE-He213 | ||
| 005 | 20240730164232.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2011 sz | s |||| 0|eng d | ||
| 020 |
_a9783031018466 _9978-3-031-01846-6 |
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| 024 | 7 |
_a10.1007/978-3-031-01846-6 _2doi |
|
| 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 |
_aIlyas, Ihab. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _983213 |
|
| 245 | 1 | 0 |
_aProbabilistic Ranking Techniques in Relational Databases _h[electronic resource] / _cby Ihab Ilyas, Mohamed Soliman. |
| 250 | _a1st ed. 2011. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2011. |
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| 300 |
_aVIII, 71 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 -- Uncertainty Models -- Query Semantics -- Methodologies -- Uncertain Rank Join -- Conclusion. | |
| 520 | _aRanking queries are widely used in data exploration, data analysis and decision making scenarios. While most of the currently proposed ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. Ranking uncertain data raises new challenges in query semantics and processing, making conventional methods inapplicable. Furthermore, the interplay between ranking and uncertainty models introduces new dimensions for ordering query results that do not exist in the traditional settings. This lecture describes new formulations and processing techniques for ranking queries on uncertain data. The formulations are based on marriage of traditional ranking semantics with possible worlds semantics under widely-adopted uncertainty models. In particular, we focus on discussing the impact of tuple-level and attribute-level uncertainty on the semantics and processing techniques of ranking queries. Under the tuple-level uncertainty model, we describe new processing techniques leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based ranking. Under the attribute-level uncertainty model, we describe new probabilistic ranking models and a set of query evaluation algorithms, including sampling-based techniques. We also discuss supporting rank join queries on uncertain data, and we show how to extend current rank join methods to handle uncertainty in scoring attributes. Table of Contents: Introduction / Uncertainty Models / Query Semantics / Methodologies / Uncertain Rank Join / Conclusion. | ||
| 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. _983215 |
| 650 | 2 | 4 |
_aData Structures and Information Theory. _931923 |
| 700 | 1 |
_aSoliman, Mohamed. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _983218 |
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| 710 | 2 |
_aSpringerLink (Online service) _983220 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031007187 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031029745 |
| 830 | 0 |
_aSynthesis Lectures on Data Management, _x2153-5426 _983222 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01846-6 |
| 912 | _aZDB-2-SXSC | ||
| 942 | _cEBK | ||
| 999 |
_c85469 _d85469 |
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