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题名/责任者:

Big data management and analytics / Brij B Gupta, Asia University, Taiwan, Mamta, Thapar Institute of Engineering and Technology, India.

出版发行项:

Singapore ; Hackensack, NJ : World Scientific, 2024.

出版发行项:

Singapore ; Hackensack, NJ : World Scientific, 2024.

ISBN:

9789811257117:

ISBN:

9811257116

载体形态项:

xxix, 257 pages : illustrations (some color) ; 24 cm

丛编说明:

World Scientific series on future computing paradigms and applications ; vol. 1

个人责任者:

Gupta, Brij, 1982- author.

附加个人名称:

Mamta, author.

论题主题:

Big data.

论题主题:

Database management.

论题主题:

Data mining.

中图法分类号:

TP311

书目附注:

Includes bibliographical references and index.

摘要附注:

"With the proliferation of information, big data management and analysis have become an indispensable part of any system to handle such amounts of data. The amount of data generated by the multitude of interconnected devices increases exponentially, making the storage and processing of these data a real challenge. Big data management and analytics have gained momentum in almost every industry, ranging from finance or healthcare. Big data can reveal key insights if handled and analyzed properly; it has great application potential to improve the working of any industry. This book covers the spectrum aspects of big data; from the preliminary level to specific case studies. It will help readers gain knowledge of the big data landscape. Highlights of the topics covered include description of the Big Data ecosystem; real-world instances of big data issues; how the Vs of Big Data (volume, velocity, variety, veracity, valence, and value) affect data collection, monitoring, storage, analysis, and reporting; structural process to get value out of Big Data and recognize the differences between a standard database management system and a big data management system. Readers will gain insights into choice of data models, data extraction, data integration to solve large data problems, data modelling using machine learning techniques, Spark's scalable machine learning techniques, modeling a big data problem into a graph database and performing scalable analytical operations over the graph and different tools and techniques for processing big data and its applications including in healthcare and finance"--

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