000			03845nam a22004575i 4500
999			_c386836 _d386836
001			386836
003			ES-MaUEC
005			20230124175940.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2011 sz | s |||| 0|eng d
020			_a9783031018794
024	7		_a10.1007/978-3-031-01879-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D32 _b2011 EB
100	1		_aSuciu, Dan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686175
245	1	0	_aProbabilistic Databases _cby Dan Suciu, Dan Olteanu, Christopher Re, Christoph Koch
250			_a1st edition 2011
264		1	_aCham _bSpringer International Publishing _c2011
300			_a1 recurso en línea (XV, 164 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		_aOverview -- Data and Query Model -- The Query Evaluation Problem -- Extensional Query Evaluation -- Intensional Query Evaluation -- Advanced Techniques.
520			_aProbabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques.
988			_aSynthesis Collection of Technology_2011
650		7	_2embne _9138966 _aBases de datos
650		7	_2embne _9405075 _aProbabilidades
650		7	_2embne _9147823 _aRecuperación de la información
700	1		_aOlteanu, Dan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686176
700	1		_aRé, Christopher _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686177
700	1		_aKoch, Christoph, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686178 _d1975-
776	0	8	_iPrinted edition: _z9783031007514
776	0	8	_iPrinted edition: _z9783031030079
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01879-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _esc _zSI