000			02966cam a2200349 i 4500
999			_c9831 _d9831
001			17708471
005			20190509110402.0
008			130423s2013 ne b 001 0 eng
010			_a 2013006421
020			_a9780124045767
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aQA76.9.D32 _bB47 2013
082	0	0	_a005.74 _222 _bB.J.P
100	1		_aBerman, Jules J.
245	1	0	_aPrinciples of big data : _bpreparing, sharing, and analyzing complex information / _cJules J Berman.
260			_aAmsterdam : _bElsevier, Morgan Kaufmann, _cc2013.
300			_axxvi, 261 p. ; _c25 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
500			_acomputer bookfair2015
504			_aIncludes bibliographical references and index (pages 247-255).
505		0	_a1. Providing structure to unstructured data -- 2. Identification, deidentification, and reidentification -- 3. Ontologies and semantics -- 4. Introspection -- 5. Data integration and software interoperability -- 6. Immutability and immortality -- 7. Measurement -- 8. Simple but powerful big data techniques -- 9. Analysis -- 10. Special considerations in big data analysis -- 11. Stepwise approach to big data analysis -- 12. Failure -- 13. Legalities -- 14. Societal issues -- 15. The future.
520			_a"Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. . Learn general methods for specifying Big Data in a way that is understandable to humans and to computers. . Avoid the pitfalls in Big Data design and analysis. . Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources"--Provided by publisher.
650		0	_aBig data.
650		0	_aDatabase management.
856			_3Abstract _uhttp://repository.fue.edu.eg/xmlui/handle/123456789/3636
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK