Data Science and Big Data Computing: Frameworks and Methodologies

ISBN 10: 3319318594 / ISBN 13: 9783319318592

Published by Springer, 2016

About this Item

Hardcover issued without dust-jacket. Clean and solid. Ships from a smoke-free home. Seller Inventory # mon0000006643

Bibliographic Details

Title: Data Science and Big Data Computing: ...

Publisher: Springer

Publication Date: 2016

Binding: hardcover

Condition: Very Good

About this title

Synopsis:

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 authoritative 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. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.

From the Back Cover:

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.