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008			220601s2021 sz | s |||| 0|eng d
020			_a9783031018787
024	7		_a10.1007/978-3-031-01878-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D3 _b2021 EB
100	1		_aPapadakis, George _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687062
245	1	4	_aThe Four Generations of Entity Resolution _cby George Papadakis, Ekaterini Ioannou, Emanouil Thanos, Themis Palpanas
250			_a1st edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XVII, 152 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 -- Entity Resolution: Past, Present, and Yet-to-Come -- Preliminaries -- Generation 1: Addressing Veracity -- Generation 2: Also Addressing Volume -- Generation 3: Also Addressing Variety -- Generation 4: Also Addressing Velocity -- Leveraging External Knowledge -- Resources for Entity Resolution -- Possible Directions for Future Work -- Bibliography -- Authors' Biographies.
520			_aEntity Resolution (ER) lies at the core of data integration and cleaning and, thus, a bulk of the research examines ways for improving its effectiveness and time efficiency. The initial ER methods primarily target Veracity in the context of structured (relational) data that are described by a schema of well-known quality and meaning. To achieve high effectiveness, they leverage schema, expert, and/or external knowledge. Part of these methods are extended to address Volume, processing large datasets through multi-core or massive parallelization approaches, such as the MapReduce paradigm. However, these early schema-based approaches are inapplicable to Web Data, which abound in voluminous, noisy, semi-structured, and highly heterogeneous information. To address the additional challenge of Variety, recent works on ER adopt a novel, loosely schema-aware functionality that emphasizes scalability and robustness to noise. Another line of present research focuses on the additional challenge of Velocity, aiming to process data collections of a continuously increasing volume. The latest works, though, take advantage of the significant breakthroughs in Deep Learning and Crowdsourcing, incorporating external knowledge to enhance the existing words to a significant extent. This synthesis lecture organizes ER methods into four generations based on the challenges posed by these four Vs. For each generation, we outline the corresponding ER workflow, discuss the state-of-the-art methods per workflow step, and present current research directions. The discussion of these methods takes into account a historical perspective, explaining the evolution of the methods over time along with their similarities and differences. The lecture also discusses the available ER tools and benchmark datasets that allow expert as well as novice users to make use of the available solutions.
988			_aSynthesis Collection of Technology_2021
650		7	_2embne _9162648 _aData mining
650		7	_2embne _9150569 _aSistemas de gestión de bases de datos
700	1		_aIoannou, Ekaterini _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687063
700	1		_aThanos, Emanouil _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687064
700	1		_aPalpanas, Themis, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687052 _d1973-
776	0	8	_iPrinted edition: _z9783031001055
776	0	8	_iPrinted edition: _z9783031007507
776	0	8	_iPrinted edition: _z9783031030062
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01878-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI