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

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Hlavní autor: Berman, Jules J.
Médium: E-Book
Jazyk: Anglais
Vydáno: Amsterdam : Elsevier, Morgan Kaufmann, [2013].
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Shrnutí: 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
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Příbuzné jednotky: Další dostupný formát: Principles of big data
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020 |a 9780124047242 
020 |a 9780124047242 
041 0 |a eng 
082 |a 005.74 
100 1 |a Berman, Jules J. 
245 1 0 |a Principles of big data :  |b preparing, sharing, and analyzing complex information   |c Jules J. Berman. 
256 |a Données textuelles 
260 |a Amsterdam :  |b Elsevier, Morgan Kaufmann,  |c [2013]. 
500 |a Titre provenant de l'écran titre 
500 |a Numérisation de l'édition de San Diego : Elsevier Science & Technology Books, 2013 
500 |a La pagination de l'édition imprimée correspondante est de 287 p. 
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 
650 |a Données massives 
650 |a Bases de données  |x Gestion 
650 |a Big data 
650 |a Database management 
776 0 |0 182880346  |t Principles of big data  |o preparing, sharing, and analyzing complex information  |f Jules J Berman,...  |c Amsterdam  |n Elsevier  |n Morgan Kaufmann  |d cop. 2013  |p 1 vol. (XXVI-261 p.) 
856 4 |u https://srvext.uco.fr/login?url=https://univ.scholarvox.com/book/88814381  |z Accès à l'E-book  
993 |a E-Book 
994 |a BNUM 
995 |a 171273117