Principles of big data : preparing, sharing, and analyzing complex information [Ressource électronique] / Jules J. Berman

PPN : 171273117Main Author : Berman, Jules J.Publication : Amsterdam : Elsevier, Morgan Kaufmann, [2013]ISBN : 978-0-124047-24-2ISBN : 978-0-12-404724-2Note : Numérisation de l'édition de San Diego : Elsevier Science & Technology Books, 2013Other edition on other media : Principles of big dataSubject - Topical Name : Données massives | Bases de données -- Gestion | Big data | Database management Online access : access : access : type : ebook
Item type Current location Call number Status Date due Barcode Item holds
En ligne Bibliothèque numérique
En ligne
005.74 (Browse shelf) Available 545416-1001
Prêt normal INSA
INSA - En ligne
Total holds: 0

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.

L'accès complet à la ressource est réservé aux usagers des établissements qui en ont fait l'acquisition

352382210:707319528 Accessible sur ScholarVox

352382210:707866456 Accessible sur ScholarVox

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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