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 changi...
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| Glavni autor: | |
|---|---|
| Format: | Livre numérique |
| Jezik: | Anglais |
| Izdano: |
Amsterdam :
Elsevier, Morgan Kaufmann
[2013].
|
| Teme: | |
| Online pristup: | Accès Université d'Orléans et IFPM Accès INSA CVL |
| Bilješka: |
Titre provenant de l'écran titre Numérisation de l'édition de San Diego : Elsevier Science & Technology Books, 2013 La pagination de l'édition imprimée correspondante est de 287 p. Cyberlibris (ScholarVox) corpus Sciences de l'ingénieur Cyberlibris (ScholarVox) corpus Sciences de l'ingénieur |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Principles of big data, preparing, sharing, and analyzing complex information, Jules J Berman,..., Amsterdam, Elsevier, Morgan Kaufmann, 2013, 1 vol. (XXVI-261 p.), 978-0-12-404576-7 |
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| 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. | ||
| 500 | |a Cyberlibris (ScholarVox) corpus Sciences de l'ingénieur | ||
| 500 | |a Cyberlibris (ScholarVox) corpus Sciences de l'ingénieur | ||
| 506 | |a L'accès complet à la ressource est réservé aux usagers des établissements qui en ont fait l'acquisition | ||
| 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 | ||
| 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 2013 |p 1 vol. (XXVI-261 p.) |z 978-0-12-404576-7 | |
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| 856 | 4 | |5 180339901:873546822 |u https://ezproxy.insa-cvl.fr/login?qurl=https://univ.scholarvox.com/book/88814381 |z Accès INSA CVL | |
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