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003			ES-MaUEC
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007			cr nn 008mamaa
008			220601s2019 sz | s |||| 0|eng d
020			_a9783031018657
024	7		_a10.1007/978-3-031-01865-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aZ666.7 _b2019 EB
100	1		_aAbedjan, Ziawasch _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686174
245	1	0	_aData Profiling _cby Ziawasch Abedjan, Lukasz Golab, Felix Naumann, Thorsten Papenbrock
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XV, 136 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 -- Discovering Metadata -- Data Profiling Tasks -- Single-Column Analysis -- Dependency Discovery -- Relaxed and Other Dependencies -- Use Cases -- Profiling Non-Relational Data -- Data Profiling Tools -- Data Profiling Challenges -- Conclusions -- Bibliography -- Authors' Biographies.
520			_aData profiling refers to the activity of collecting data about data, {i.e.}, metadata. Most IT professionals and researchers who work with data have engaged in data profiling, at least informally, to understand and explore an unfamiliar dataset or to determine whether a new dataset is appropriate for a particular task at hand. Data profiling results are also important in a variety of other situations, including query optimization, data integration, and data cleaning. Simple metadata are statistics, such as the number of rows and columns, schema and datatype information, the number of distinct values, statistical value distributions, and the number of null or empty values in each column. More complex types of metadata are statements about multiple columns and their correlation, such as candidate keys, functional dependencies, and other types of dependencies. This book provides a classification of the various types of profilable metadata, discusses popular data profiling tasks, and surveys state-of-the-art profiling algorithms. While most of the book focuses on tasks and algorithms for relational data profiling, we also briefly discuss systems and techniques for profiling non-relational data such as graphs and text. We conclude with a discussion of data profiling challenges and directions for future work in this area.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9159987 _aMetadatos
650		7	_2embne _9162648 _aData mining
700	1		_aGolab, Lukasz, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686171 _d1978-
700	1		_aNaumann, Felix _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686172
700	1		_aPapenbrock, Thorsten _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686173
776	0	8	_iPrinted edition: _z9783031000928
776	0	8	_iPrinted edition: _z9783031007378
776	0	8	_iPrinted edition: _z9783031029936
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01865-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _esc _zSI