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007			cr nn 008mamaa
008			220601s2018 sz | s |||| 0|eng d
020			_a9783031018633
024	7		_a10.1007/978-3-031-01863-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D3 _b2018 EB
100	1		_aGao, Yunjun _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687031
245	1	0	_aQuery Processing over Incomplete Databases _cby Yunjun Gao, Xiaoye Miao
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XV, 106 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		_aPreface -- Acknowledgments -- Introduction -- Handling Incomplete Data Methods -- Query Semantics on Incomplete Data -- Advanced Techniques -- Conclusions -- Bibliography -- Authors' Biographies.
520			_aIncomplete data is part of life and almost all areas of scientific studies. Users tend to skip certain fields when they fill out online forms; participants choose to ignore sensitive questions on surveys; sensors fail, resulting in the loss of certain readings; publicly viewable satellite map services have missing data in many mobile applications; and in privacy-preserving applications, the data is incomplete deliberately in order to preserve the sensitivity of some attribute values. Query processing is a fundamental problem in computer science, and is useful in a variety of applications. In this book, we mostly focus on the query processing over incomplete databases, which involves finding a set of qualified objects from a specified incomplete dataset in order to support a wide spectrum of real-life applications. We first elaborate the three general kinds of methods of handling incomplete data, including (i) discarding the data with missing values, (ii) imputation for the missing values, and (iii) just depending on the observed data values. For the third method type, we introduce the semantics of k-nearest neighbor (kNN) search, skyline query, and top-k dominating query on incomplete data, respectively. In terms of the three representative queries over incomplete data, we investigate some advanced techniques to process incomplete data queries, including indexing, pruning as well as crowdsourcing techniques.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9146720 _aEstimación estadística
650		7	_2embne _9147823 _aRecuperación de la información
650		7	_2embne _9150569 _aSistemas de gestión de bases de datos
700	1		_aMiao, Xiaoye _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687032
776	0	8	_iPrinted edition: _z9783031000904
776	0	8	_iPrinted edition: _z9783031007354
776	0	8	_iPrinted edition: _z9783031029912
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01863-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI