國立高雄大學圖資館 |

Language: English

Back

Data science and big data computingf...

Mahmood, Zaigham.

Data science and big data computingframeworks and methodologies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science and big data computingedited by Zaigham Mahmood.
Reminder of title:	frameworks and methodologies /
other author:	Mahmood, Zaigham.
Published:	Cham :Springer International Publishing :2016.
Description:	xxi, 319 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Big data.
Online resource:	http://dx.doi.org/10.1007/978-3-319-31861-5
ISBN:	9783319318615$q(electronic bk.)

Data science and big data computingframeworks and methodologies /
Data science and big data computingframeworks and methodologies /[electronic resource] :edited by Zaigham Mahmood. - Cham :Springer International Publishing :2016. - xxi, 319 p. :ill., digital ;24 cm.

Part I: Data Science Applications and Scenarios -- An Interoperability Framework and Distributed Platform for Fast Data Applications -- Complex Event Processing Framework for Big Data Applications -- Agglomerative Approaches for Partitioning of Networks in Big Data Scenarios -- Identifying Minimum-Sized Influential Vertices on Large-Scale Weighted Graphs: A Big Data Perspective -- Part II: Big Data Modelling and Frameworks -- A Unified Approach to Data Modelling and Management in Big Data Era -- Interfacing Physical and Cyber Worlds: A Big Data Perspective -- Distributed Platforms and Cloud Services: Enabling Machine Learning for Big Data -- An Analytics Driven Approach to Identify Duplicate Bug Records in Large Data Repositories -- Part III: Big Data Tools and Analytics -- Large Scale Data Analytics Tools: Apache Hive, Pig and HBase -- Big Data Analytics: Enabling Technologies and Tools -- A Framework for Data Mining and Knowledge Discovery in Cloud Computing -- Feature Selection for Adaptive Decision Making in Big Data Analytics -- Social Impact and Social Media Analysis Relating to Big Data.

This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by an authoritative collection of thirty-six researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Topics and features: Reviews a framework for fast data applications, a technique for complex event processing, and a selection of agglomerative approaches for partitioning of networks Discusses a big data approach to identifying minimum-sized influential vertices from large-scale weighted graphs Introduces a unified approach to data modeling and management, and offers a distributed computing perspective on interfacing physical and cyber worlds Presents techniques for machine learning in the context of big data, and describes an analytics-driven approach to identifying duplicate records in large data repositories Examines various enabling technologies and tools for data mining, including Apache Hadoop Proposes a novel framework for data extraction and knowledge discovery, and provides case studies on adaptive decision making and social media analysis This comprehensive volume is a valuable reference for researchers, lecturers and students interested in data science and big data, in addition to professionals seeking to adopt the latest approaches in data analytics to gain business intelligence for strategic decision-making.

ISBN: 9783319318615$q(electronic bk.)

Standard No.: 10.1007/978-3-319-31861-5doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Data science and big data computingframeworks and methodologies /
LDR:03733nmm a2200337 a 4500 001 492771
003 DE-He213
005 20161215091630.0
006 m d
007 cr nn 008maaau
008 170220s2016 gw s 0 eng d
020 $a 9783319318615$q(electronic bk.)
020 $a 9783319318592$q(paper)
024 7 $a 10.1007/978-3-319-31861-5 $2 doi
035 $a 978-3-319-31861-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45
072 7 $a UYZM $2 bicssc
072 7 $a UKR $2 bicssc
072 7 $a BUS083000 $2 bisacsh
072 7 $a COM032000 $2 bisacsh
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b D232 2016
245 0 0 $a Data science and big data computing $h [electronic resource] : $b frameworks and methodologies / $c edited by Zaigham Mahmood.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xxi, 319 p. : $b ill., digital ; $c 24 cm.
505 0 $a Part I: Data Science Applications and Scenarios -- An Interoperability Framework and Distributed Platform for Fast Data Applications -- Complex Event Processing Framework for Big Data Applications -- Agglomerative Approaches for Partitioning of Networks in Big Data Scenarios -- Identifying Minimum-Sized Influential Vertices on Large-Scale Weighted Graphs: A Big Data Perspective -- Part II: Big Data Modelling and Frameworks -- A Unified Approach to Data Modelling and Management in Big Data Era -- Interfacing Physical and Cyber Worlds: A Big Data Perspective -- Distributed Platforms and Cloud Services: Enabling Machine Learning for Big Data -- An Analytics Driven Approach to Identify Duplicate Bug Records in Large Data Repositories -- Part III: Big Data Tools and Analytics -- Large Scale Data Analytics Tools: Apache Hive, Pig and HBase -- Big Data Analytics: Enabling Technologies and Tools -- A Framework for Data Mining and Knowledge Discovery in Cloud Computing -- Feature Selection for Adaptive Decision Making in Big Data Analytics -- Social Impact and Social Media Analysis Relating to Big Data.
520 $a This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by an authoritative collection of thirty-six researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Topics and features: Reviews a framework for fast data applications, a technique for complex event processing, and a selection of agglomerative approaches for partitioning of networks Discusses a big data approach to identifying minimum-sized influential vertices from large-scale weighted graphs Introduces a unified approach to data modeling and management, and offers a distributed computing perspective on interfacing physical and cyber worlds Presents techniques for machine learning in the context of big data, and describes an analytics-driven approach to identifying duplicate records in large data repositories Examines various enabling technologies and tools for data mining, including Apache Hadoop Proposes a novel framework for data extraction and knowledge discovery, and provides case studies on adaptive decision making and social media analysis This comprehensive volume is a valuable reference for researchers, lecturers and students interested in data science and big data, in addition to professionals seeking to adopt the latest approaches in data analytics to gain business intelligence for strategic decision-making.
650 0 $a Big data. $3 609582
650 0 $a Computer networks. $3 181923
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Management of Computing and Information Systems. $3 274191
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Computer Communication Networks. $3 218087
700 1 $a Mahmood, Zaigham. $3 539371
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-31861-5
950 $a Computer Science (Springer-11645)