Data, engineering and applications. Volume 1 /

Data, engineering and applications. Volume 1 /

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Bibliographic Details
Imprint:	Singapore : Springer, 2019.
Description:	1 online resource (viii, 191 pages) : illustrations (some color)
Language:	English
Subject:	Data mining. Machine learning. Data mining. Machine learning.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11852169

Hidden Bibliographic Details
Other authors / contributors:	Shukla, Rajesh K., editor. Agrawal, Jitendra, editor. Sharma, Sanjeev (Information technology executive), editor. Tomer, Geetam Singh, editor.
ISBN:	9789811363474 9811363471 9789811363481 981136348X 9789811363498 9811363498 9789811363467 9811363463
Digital file characteristics:	text file PDF
Notes:	Online resource; title from PDF title page (SpringerLink, viewed March 27, 2019).
Summary:	This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications.
Other form:	Print version: Data, engineering and applications. Volume 1. Singapore : Springer, 2019 9811363463 9789811363467
Standard no.:	10.1007/978-981-13-6347-4

Description
Summary:	This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications. <br>
Physical Description:	1 online resource (viii, 191 pages) : illustrations (some color)
ISBN:	9789811363474 9811363471 9789811363481 981136348X 9789811363498 9811363498 9789811363467 9811363463