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007			cr nn 008mamaa
008			220601s2013 sz | s |||| 0|eng d
020			_a9783031018978
024	7		_a10.1007/978-3-031-01897-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D3 _b2013 EB
100	1		_aGanti, Venkatesh _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687219
245	1	0	_aData Cleaning _cby Venkatesh Ganti, Anish Das Sarma
250			_a1st edition 2013
264		1	_aCham _bSpringer International Publishing _c2013
300			_a1 recurso en línea (XV, 69 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 -- Technological Approaches -- Similarity Functions -- Operator: Similarity Join -- Operator: Clustering -- Operator: Parsing -- Task: Record Matching -- Task: Deduplication -- Data Cleaning Scripts -- Conclusion -- Bibliography -- Authors' Biographies.
520			_aData warehouses consolidate various activities of a business and often form the backbone for generating reports that support important business decisions. Errors in data tend to creep in for a variety of reasons. Some of these reasons include errors during input data collection and errors while merging data collected independently across different databases. These errors in data warehouses often result in erroneous upstream reports, and could impact business decisions negatively. Therefore, one of the critical challenges while maintaining large data warehouses is that of ensuring the quality of data in the data warehouse remains high. The process of maintaining high data quality is commonly referred to as data cleaning. In this book, we first discuss the goals of data cleaning. Often, the goals of data cleaning are not well defined and could mean different solutions in different scenarios. Toward clarifying these goals, we abstract out a common set of data cleaning tasks that often need to be addressed. This abstraction allows us to develop solutions for these common data cleaning tasks. We then discuss a few popular approaches for developing such solutions. In particular, we focus on an operator-centric approach for developing a data cleaning platform. The operator-centric approach involves the development of customizable operators that could be used as building blocks for developing common solutions. This is similar to the approach of relational algebra for query processing. The basic set of operators can be put together to build complex queries. Finally, we discuss the development of custom scripts which leverage the basic data cleaning operators along with relational operators to implement effective solutions for data cleaning tasks.
988			_aSynthesis Collection of Technology_2013
650		7	_2embne _9150569 _aSistemas de gestión de bases de datos
650		7	_2embne _9141180 _aProceso de datos
700	1		_aDas Sarma, Anish _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687220
776	0	8	_iPrinted edition: _z9783031007699
776	0	8	_iPrinted edition: _z9783031030253
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01897-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI