國立高雄大學圖資館 |

Language: English

Back

Guide to big data applications

SpringerLink (Online service)

Guide to big data applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Guide to big data applicationsedited by S. Srinivasan.
other author:	Srinivasan, S.
Published:	Cham :Springer International Publishing :2018.
Description:	xvii, 565 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Big data.
Online resource:	http://dx.doi.org/10.1007/978-3-319-53817-4
ISBN:	9783319538174$q(electronic bk.)

Guide to big data applications
Guide to big data applications[electronic resource] /edited by S. Srinivasan. - Cham :Springer International Publishing :2018. - xvii, 565 p. :ill., digital ;24 cm. - Studies in big data,v.262197-6503 ;. - Studies in big data ;v.1..

Introduction -- Big Data Analytics -- Big Data and Social Media -- Use of Cloud Computing for Big Data in Business -- Economic Data Analysis Related to Developing Countries -- High Performance Computing and Big Data -- Big Data Applications in Physics -- Big Data Applications in Chemistry -- Big Data Applications in Mathematics -- Big Data Applications in Biology -- Big Data Applications in Engineering -- Big Data Applications in Meteorology -- Big Data Applications in Environmental Science -- Big Data Applications in Energy -- Security Applications for Big Data -- Big Data Applications in Network Traffic Analysis -- Big Data Applications in Supply Chain Logistics -- Big Data Applications in Healthcare -- Big Data Applications in Cancer Research -- Impact of Big Data in Marketing -- Use of Big Data in Banking -- Using Big Data for Fraud Detection in Accounting -- Using Big Data for Supply Chain Management -- Privacy Implications of Big Data -- Legal Perspectives of Big Data -- Ethical Handling of Big Data in Practical Uses -- Conclusion.

This handbook brings together a variety of approaches to the uses of big data in multiple fields, primarily science, medicine, and business. This single resource features contributions from researchers around the world from a variety of fields, where they share their findings and experience. This book is intended to help spur further innovation in big data. The research is presented in a way that allows readers, regardless of their field of study, to learn from how applications have proven successful and how similar applications could be used in their own field. Contributions stem from researchers in fields such as physics, biology, energy, healthcare, and business. The contributors also discuss important topics such as fraud detection, privacy implications, legal perspectives, and ethical handling of big data.

ISBN: 9783319538174$q(electronic bk.)

Standard No.: 10.1007/978-3-319-53817-4doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.B45 / G853 2018

Dewey Class. No.: 005.7

Guide to big data applications
LDR:02849nmm a2200325 a 4500 001 526497
003 DE-He213
005 20170526114748.0
006 m d
007 cr nn 008maaau
008 181011s2018 gw s 0 eng d
020 $a 9783319538174$q(electronic bk.)
020 $a 9783319538167$q(paper)
024 7 $a 10.1007/978-3-319-53817-4 $2 doi
035 $a 978-3-319-53817-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45 $b G853 2018
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b G946 2018
245 0 0 $a Guide to big data applications $h [electronic resource] / $c edited by S. Srinivasan.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xvii, 565 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6503 ; $v v.26
505 0 $a Introduction -- Big Data Analytics -- Big Data and Social Media -- Use of Cloud Computing for Big Data in Business -- Economic Data Analysis Related to Developing Countries -- High Performance Computing and Big Data -- Big Data Applications in Physics -- Big Data Applications in Chemistry -- Big Data Applications in Mathematics -- Big Data Applications in Biology -- Big Data Applications in Engineering -- Big Data Applications in Meteorology -- Big Data Applications in Environmental Science -- Big Data Applications in Energy -- Security Applications for Big Data -- Big Data Applications in Network Traffic Analysis -- Big Data Applications in Supply Chain Logistics -- Big Data Applications in Healthcare -- Big Data Applications in Cancer Research -- Impact of Big Data in Marketing -- Use of Big Data in Banking -- Using Big Data for Fraud Detection in Accounting -- Using Big Data for Supply Chain Management -- Privacy Implications of Big Data -- Legal Perspectives of Big Data -- Ethical Handling of Big Data in Practical Uses -- Conclusion.
520 $a This handbook brings together a variety of approaches to the uses of big data in multiple fields, primarily science, medicine, and business. This single resource features contributions from researchers around the world from a variety of fields, where they share their findings and experience. This book is intended to help spur further innovation in big data. The research is presented in a way that allows readers, regardless of their field of study, to learn from how applications have proven successful and how similar applications could be used in their own field. Contributions stem from researchers in fields such as physics, biology, energy, healthcare, and business. The contributors also discuss important topics such as fraud detection, privacy implications, legal perspectives, and ethical handling of big data.
650 0 $a Big data. $3 609582
650 0 $a Application software. $3 200645
650 1 4 $a Engineering. $3 210888
650 2 4 $a Communications Engineering, Networks. $3 273745
650 2 4 $a Computer Systems Organization and Communication Networks. $3 273709
650 2 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Big Data/Analytics. $3 742047
650 2 4 $a Health Informatics. $3 274212
650 2 4 $a International IT and Media Law, Intellectual Property Law. $3 560593
700 1 $a Srinivasan, S. $3 681468
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Studies in big data ; $v v.1. $3 675357
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-53817-4
950 $a Engineering (Springer-11647)