國立高雄大學圖資館 |

Language: English

Back

Big data analyticssystems, algorithm...

Prabhu, C. S. R.

Big data analyticssystems, algorithms, applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big data analyticsby C. S. R. Prabhu ... [et al.].
Reminder of title:	systems, algorithms, applications /
other author:	Prabhu, C. S. R.
Published:	Singapore :Springer Singapore :2019.
Description:	xxvi, 412 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Big data.
Online resource:	https://doi.org/10.1007/978-981-15-0094-7
ISBN:	9789811500947$q(electronic bk.)

Big data analyticssystems, algorithms, applications /
Big data analyticssystems, algorithms, applications /[electronic resource] :by C. S. R. Prabhu ... [et al.]. - Singapore :Springer Singapore :2019. - xxvi, 412 p. :ill., digital ;24 cm.

Big Data -- Intelligent Systems -- Analytics Models for Data Science -- Big Data Tools - Hadoop Eco System -- Predictive Modeling for Unstructured Data -- Machine Learning Algorithms for Big Data -- Social Semantic Web Mining and Big Data Analytics -- Internet of Things (IoT) and Big Data Analytics -- Big Data Analytics for Financial and Services Banking -- Big Data Analytics Techniques in Capital Market Use Cases.

This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning - including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.

ISBN: 9789811500947$q(electronic bk.)

Standard No.: 10.1007/978-981-15-0094-7doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Big data analyticssystems, algorithms, applications /
LDR:02900nmm a2200325 a 4500 001 567891
003 DE-He213
005 20191014130017.0
006 m d
007 cr nn 008maaau
008 200611s2019 si s 0 eng d
020 $a 9789811500947$q(electronic bk.)
020 $a 9789811500930$q(paper)
024 7 $a 10.1007/978-981-15-0094-7 $2 doi
035 $a 978-981-15-0094-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45
072 7 $a UN $2 bicssc
072 7 $a COM021000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b B592 2019
245 0 0 $a Big data analytics $h [electronic resource] : $b systems, algorithms, applications / $c by C. S. R. Prabhu ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a xxvi, 412 p. : $b ill., digital ; $c 24 cm.
505 0 $a Big Data -- Intelligent Systems -- Analytics Models for Data Science -- Big Data Tools - Hadoop Eco System -- Predictive Modeling for Unstructured Data -- Machine Learning Algorithms for Big Data -- Social Semantic Web Mining and Big Data Analytics -- Internet of Things (IoT) and Big Data Analytics -- Big Data Analytics for Financial and Services Banking -- Big Data Analytics Techniques in Capital Market Use Cases.
520 $a This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning - including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
650 0 $a Big data. $3 609582
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
700 1 $a Prabhu, C. S. R. $3 837234
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-15-0094-7
950 $a Computer Science (Springer-11645)