Principles of big data : preparing, sharing, and analyzing complex information

"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...

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Hlavní autor: Berman, Jules J., 1950- (Autor) 
Korporace: EBSCOhost (online služba) (Distributor) 
ScienceDirect (online služba) (Distributor) 
Médium: E-kniha
Jazyk:angličtina
Vydáno: Amsterdam ; Boston ; Heidelberg ; London ; New York ; Oxford ; Paris ; San Diego ; San Francisco ; Singapore ; Sydney ; Tokyo : Morgan Kaufmann is an imprint of Elsevier, 2013
Žánr/forma:příručky
elektronické knihy
ISBN:9780124047242
0124047246
978-0-12-404576-7
0-12-404576-6
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On-line přístup:Plný text
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100 1 |a Berman, Jules J.,  |d 1950-  |7 ntk2013799502  |4 aut 
245 1 0 |a Principles of big data :  |b preparing, sharing, and analyzing complex information /  |c Jules J. Berman 
264 1 |a Amsterdam ;  |a Boston ;  |a Heidelberg ;  |a London ;  |a New York ;  |a Oxford ;  |a Paris ;  |a San Diego ;  |a San Francisco ;  |a Singapore ;  |a Sydney ;  |a Tokyo :  |b Morgan Kaufmann is an imprint of Elsevier,  |c [2013] 
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505 0 |a 1. 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 
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