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Big data :: principles and paradigms /

Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications....

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Weitere Verfasser:	Buyya, Rajkumar, 1970- (HerausgeberIn), Calheiros, Rodrigo N. (HerausgeberIn), Vahid Dastjerdi, Amir (HerausgeberIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Cambridge, MA : Morgan Kaufmann is an imprint of Elsevier, [2016]
Schlagworte:	Big data. Données volumineuses. COMPUTERS > Data Processing. Big data dissertations. Academic theses Academic theses. Thèses et écrits académiques.
Online-Zugang:	Volltext Volltext
Zusammenfassung:	Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data's full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.
Beschreibung:	1 online resource (xxv, 468 pages) : illustrations (some color)
Bibliographie:	Includes bibliographical references and index.
ISBN:	9780128093467 0128093463

Internformat

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520			\|a Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data's full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.
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504			\|a Includes bibliographical references and index.
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spelling	Big data : principles and paradigms / edited by Rajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjerdi. Cambridge, MA : Morgan Kaufmann is an imprint of Elsevier, [2016] 1 online resource (xxv, 468 pages) : illustrations (some color) text txt rdacontent computer c rdamedia online resource cr rdacarrier Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data's full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues. Online resource; title from PDF title page (ScienceDirect, viewed July 11, 2016). Includes bibliographical references and index. Part I. Big data science -- part II. Big data infrastructures and platforms -- part III. Big data security and privacy -- part IV. Big data applications. Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Données volumineuses. COMPUTERS Data Processing. bisacsh Big data fast dissertations. aat Academic theses fast Academic theses. lcgft http://id.loc.gov/authorities/genreForms/gf2014026039 Thèses et écrits académiques. rvmgf Buyya, Rajkumar, 1970- editor. https://id.oclc.org/worldcat/entity/E39PBJxRDCwvFrpPwp6Whbf6rq Calheiros, Rodrigo N., editor. Vahid Dastjerdi, Amir, editor. has work: Big data (Text) https://id.oclc.org/worldcat/entity/E39PCFxpJDBjQBW9xXqHQy64mb https://id.oclc.org/worldcat/ontology/hasWork Print version: Big data. Cambridge, MA : Morgan Kaufmann is an imprint of Elsevier, [2016] 0128053941 9780128053942 (OCoLC)946605551 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1145031 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/book/9780128053942 Volltext
spellingShingle	Big data : principles and paradigms / Part I. Big data science -- part II. Big data infrastructures and platforms -- part III. Big data security and privacy -- part IV. Big data applications. Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Données volumineuses. COMPUTERS Data Processing. bisacsh Big data fast
subject_GND	http://id.loc.gov/authorities/subjects/sh2012003227 http://id.loc.gov/authorities/genreForms/gf2014026039
title	Big data : principles and paradigms /
title_auth	Big data : principles and paradigms /
title_exact_search	Big data : principles and paradigms /
title_full	Big data : principles and paradigms / edited by Rajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjerdi.
title_fullStr	Big data : principles and paradigms / edited by Rajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjerdi.
title_full_unstemmed	Big data : principles and paradigms / edited by Rajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjerdi.
title_short	Big data :
title_sort	big data principles and paradigms
title_sub	principles and paradigms /
topic	Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Données volumineuses. COMPUTERS Data Processing. bisacsh Big data fast
topic_facet	Big data. Données volumineuses. COMPUTERS Data Processing. Big data dissertations. Academic theses Academic theses. Thèses et écrits académiques.
url	https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1145031 https://www.sciencedirect.com/science/book/9780128053942
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