Data Science and Big Data Computing : Frameworks and Methodologies / edited by Zaigham Mahmood.

Format:

Book

Contributor:

Mahmood, Zaigham, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Management information systems.

Computer science.

Data mining.

Computer networks.

Management of Computing and Information Systems.

Data Mining and Knowledge Discovery.

Computer Communication Networks.

Local Subjects:

Management of Computing and Information Systems.

Data Mining and Knowledge Discovery.

Computer Communication Networks.

Physical Description:

1 online resource (XXI, 319 pages) : 68 illustrations

Edition:

First edition 2016.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2016.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-319-31861-5

9783319318615

Access Restriction:

Restricted for use by site license.