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Large Scale Data Analytics (Studies in Computational Intelligence, nr. 806)

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Notă GoodReads:
en Limba Engleză Hardback – 25 Jan 2019
This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness.
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Specificații

ISBN-13: 9783030038915
ISBN-10: 3030038912
Pagini: 190
Ilustrații: IX, 89 p.
Dimensiuni: 155 x 235 mm
Greutate: 0.45 kg
Ediția: 1st ed. 2019
Editura: Springer
Colecția Springer
Seriile Studies in Computational Intelligence , Data, Semantics and Cloud Computing

Locul publicării: Cham, Switzerland

Cuprins

Introduction.- Background.- Large Scale Data Analytics.- Query Framework.- Results and Discussion.- Conclusion and Future Works.

Textul de pe ultima copertă

This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness.

Caracteristici

Presents large-scale protein data analytics
Introduces a language integrated query framework for big data
Provides efficient data restructuring of petascale data sources