000			04098nam a22005655i 4500
001			978-3-030-39105-8
003			DE-He213
005			20240423125114.0
007			cr nn 008mamaa
008			200316s2020 sz | s |||| 0|eng d
020			_a9783030391058 _9978-3-030-39105-8
024	7		_a10.1007/978-3-030-39105-8 _2doi
050		4	_aQA76.9.B45
072		7	_aUN _2bicssc
072		7	_aCOM021000 _2bisacsh
072		7	_aUN _2thema
082	0	4	_a005.7 _223
100	1		_aLuengo, Julián. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aBig Data Preprocessing _h[electronic resource] : _bEnabling Smart Data / _cby Julián Luengo, Diego García-Gil, Sergio Ramírez-Gallego, Salvador García, Francisco Herrera.
250			_a1st ed. 2020.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020.
300			_aXIII, 186 p. 57 illus., 54 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_a1. Introduction -- 2. Big Data: Technologies and Tools -- 3. Smart Data -- 4. Dimensionality Reduction for Big Data -- 5. Data Reduction for Big Data -- 6. Imperfect Big Data -- 7. Big Data Discretization -- 8. Imbalanced Data Preprocessing for Big Data -- 9. Big Data Software -- 10. Final Thoughts: From Big Data to Smart Data.-.
520			_aThis book offers a comprehensible overview of Big Data Preprocessing, which includes a formal description of each problem. It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems. This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and to achieve Smart Data the preprocessing is a key step, where the imperfections, integration tasks and other processes are carried out to eliminate superfluous information. The authors present the concept of Smart Data through data preprocessing in Big Data scenarios and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on the cloud. Finally, this book provides some novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing techniques applied and some other open problems. Practitioners and data scientists who work in this field, and want to introduce themselves to preprocessing in large data volume scenarios will want to purchase this book. Researchers that work in this field, who want to know which algorithms are currently implemented to help their investigations, may also be interested in this book.
650		0	_aBig data.
650		0	_aMachine learning.
650		0	_aComputer networks .
650	1	4	_aBig Data.
650	2	4	_aMachine Learning.
650	2	4	_aComputer Communication Networks.
700	1		_aGarcía-Gil, Diego. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aRamírez-Gallego, Sergio. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aGarcía, Salvador. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aHerrera, Francisco. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030391041
776	0	8	_iPrinted edition: _z9783030391065
776	0	8	_iPrinted edition: _z9783030391072
856	4	0	_uhttps://doi.org/10.1007/978-3-030-39105-8
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c174294 _d174294