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008			220601s2014 sz | o |||| 0|eng d
020			_a9783031018510
024	7		_a10.1007/978-3-031-01851-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D3 _b2014 EB
100	1		_aAugsten, Nikolaus _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686317
245	1	0	_aSimilarity Joins in Relational Database Systems _cby Nikolaus Augsten, Michael Bohlen
250			_a1st edition 2014
264		1	_aCham _bSpringer International Publishing _c2014
300			_a1 recurso en línea (XVII, 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 -- Data Types -- Edit-Based Distances -- Token-Based Distances -- Query Processing Techniques -- Filters for Token Equality Joins -- Conclusion -- Bibliography -- Authors' Biographies -- Index.
520			_aState-of-the-art database systems manage and process a variety of complex objects, including strings and trees. For such objects equality comparisons are often not meaningful and must be replaced by similarity comparisons. This book describes the concepts and techniques to incorporate similarity into database systems. We start out by discussing the properties of strings and trees, and identify the edit distance as the de facto standard for comparing complex objects. Since the edit distance is computationally expensive, token-based distances have been introduced to speed up edit distance computations. The basic idea is to decompose complex objects into sets of tokens that can be compared efficiently. Token-based distances are used to compute an approximation of the edit distance and prune expensive edit distance calculations. A key observation when computing similarity joins is that many of the object pairs, for which the similarity is computed, are very different from each other. Filters exploit this property to improve the performance of similarity joins. A filter preprocesses the input data sets and produces a set of candidate pairs. The distance function is evaluated on the candidate pairs only. We describe the essential query processing techniques for filters based on lower and upper bounds. For token equality joins we describe prefix, size, positional and partitioning filters, which can be used to avoid the computation of small intersections that are not needed since the similarity would be too low.
988			_aSynthesis Collection of Technology_2014
650		7	_2embne _9150569 _aSistemas de gestión de bases de datos
700	1		_aBöhlen, Michael H. _d1964- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686318
776	0	8	_iPrinted edition: _z9783031007231
776	0	8	_iPrinted edition: _z9783031029790
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01851-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eb _zSI