000			03321nam a22003975i 4500
999			_c387866 _d387866
001			387866
003			ES-MaUEC
005			20230425125935.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230425s2011 sz | s |||| 0|eng d
020			_a9783031018466
024	7		_a10.1007/978-3-031-01846-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D32 _b2011 EB
100	1		_aIlyas, Ihab F. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688256
245	1	0	_aProbabilistic Ranking Techniques in Relational Databases _cby Ihab Ilyas, Mohamed Soliman
250			_a1st edition 2011
264		1	_aCham _bSpringer International Publishing _c2011
300			_a1 recurso en línea (VIII, 71 páginas)
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 -- 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.
988			_aSynthesis Collection of Technology_2011
700	1		_aSoliman, Mohamed A. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688257
776	0	8	_iPrinted edition: _z9783031007187
776	0	8	_iPrinted edition: _z9783031029745
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01846-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI