Cho, Chung Yik.
Large Scale Data Analytics [electronic resource] / by Chung Yik Cho, Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu. - 1st ed. 2019. - IX, 89 p. online resource. - Data, Semantics and Cloud Computing, 806 2524-6607 ; . - Data, Semantics and Cloud Computing, 806 .
Introduction -- Background -- Large Scale Data Analytics -- Query Framework -- Results and Discussion -- Conclusion and Future Works.
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.
9783030038922
10.1007/978-3-030-03892-2 doi
Engineering mathematics.
Engineering—Data processing.
Mathematical and Computational Engineering Applications.
TA329-348 TA345-345.5
620
Large Scale Data Analytics [electronic resource] / by Chung Yik Cho, Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu. - 1st ed. 2019. - IX, 89 p. online resource. - Data, Semantics and Cloud Computing, 806 2524-6607 ; . - Data, Semantics and Cloud Computing, 806 .
Introduction -- Background -- Large Scale Data Analytics -- Query Framework -- Results and Discussion -- Conclusion and Future Works.
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.
9783030038922
10.1007/978-3-030-03892-2 doi
Engineering mathematics.
Engineering—Data processing.
Mathematical and Computational Engineering Applications.
TA329-348 TA345-345.5
620