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008			220601s2019 sz | s |||| 0|eng d
020			_a9783031018664
024	7		_a10.1007/978-3-031-01866-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D3 _b2019 EB
100	1		_aLissandrini, Matteo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687050
245	1	0	_aData Exploration Using Example-Based Methods _cby Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XIV, 146 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 -- Relational Data -- Graph Data -- Textual Data -- Unifying Example-Based Approaches -- Online Learning -- The Road Ahead -- Conclusions -- Bibliography -- Authors' Biographies.
520			_aData usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes challenging. Thus, being able to perform exploratory analyses in the data with the intent of having an immediate glimpse on some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicate declarative languages (such as SQL) and mechanisms, and at the same time retain the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or the analyst, circumvents query languages by using examples as input. An example is a representative of the intended results, or in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind, but may not able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when the task is particularly challenging like finding duplicate items, or simply when they are exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how that different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data. The book presents also the challenges and the new frontiers of machine learning in online settings which recently attracted the attention of the database community. The lecture concludes with a vision for further research and applications in this area.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9150569 _aSistemas de gestión de bases de datos
650		7	_2embne _9147823 _aRecuperación de la información
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aMottin, Davide _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687051
700	1		_aPalpanas, Themis, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687052 _d1973-
700	1		_aVelegrakis, Yannis, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687053 _d1973-
776	0	8	_iPrinted edition: _z9783031000935
776	0	8	_iPrinted edition: _z9783031007385
776	0	8	_iPrinted edition: _z9783031029943
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01866-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI