Fundamentals of Big Data Network Analysis for Research and Industry - Hardcover

Lee, Hyunjoung; Sohn, Il

Hardcover

ISBN 10: 1119015588 ISBN 13: 9781119015581

Publisher: Wiley, 2016

Synopsis

Presents the methodology of big data analysis using examples from research and industry

There are large amounts of data everywhere, and the ability to pick out crucial information is increasingly important. Contrary to popular belief, not all information is useful; big data network analysis assumes that data is not only large, but also meaningful, and this book focuses on the fundamental techniques required to extract essential information from vast datasets.

Featuring case studies drawn largely from the iron and steel industries, this book offers practical guidance which will enable readers to easily understand big data network analysis. Particular attention is paid to the methodology of network analysis, offering information on the method of data collection, on research design and analysis, and on the interpretation of results.  A variety of programs including UCINET, NetMiner, R, NodeXL, and Gephi for network analysis are covered in detail.

Fundamentals of Big Data Network Analysis for Research and Industry looks at big data from a fresh perspective, and provides a new approach to data analysis.

This book:

• Explains the basic concepts in understanding big data and filtering meaningful data
• Presents big data analysis within the networking perspective
• Features methodology applicable to research and industry
• Describes in detail the social relationship between big data and its implications
• Provides insight into identifying patterns and relationships between seemingly unrelated big data

Fundamentals of Big Data Network Analysis for Research and Industry will prove a valuable resource for analysts, research engineers, industrial engineers, marketing professionals, and any individuals dealing with accumulated large data whose interest is to analyze and identify potential relationships among data sets.

About this title

About the Author

Hyunjoung Lee, Institute of Green Technology, Yonsei University, Republic of Korea.

Il Sohn, Material Science and Engineering, Yonsei University, Republic of Korea.

From the Back Cover

Fundamentals of Big Data Network Analysis for Research and Industry

Hyunjoung Lee, Institute of Green Technology, Yonsei University, Republic of Korea
Il Sohn, Material Science and Engineering, Yonsei University, Republic of Korea

Presents the methodology of big data analysis using examples from research and industry

There are large amounts of data everywhere, and the ability to pick out crucial information is increasingly important. Contrary to popular belief, not all information is useful; big data network analysis assumes that data are not only large but also meaningful, and this book focuses on the fundamental techniques required to extract essential information from vast data sets.

Featuring case studies drawn largely from the iron and steel industries, this book offers practical guidance that will enable readers to easily understand big data network analysis. Particular attention is paid to the methodology of network analysis, offering information on the method of data collection, on research design and analysis, and on the interpretation of results. A variety of programs including UCINET, NetMiner, R, NodeXL, and Gephi for network analysis are covered in detail.

Fundamentals of Big Data Network Analysis for Research and Industry looks at big data from a fresh perspective and provides a new approach to data analysis.

This book:

• Explains the basic concepts in understanding big data and filtering meaningful data
• Presents big data analysis within the networking perspective
• Features methodology applicable to research and industry
• Describes in detail the social relationship between big data and its implications
• Provides insight into identifying patterns and relationships between seemingly unrelated big data

Fundamentals of Big Data Network Analysis for Research and Industry will prove a valuable resource for analysts, research engineers, industrial engineers, marketing professionals, and any individuals dealing with accumulated large data whose interest is to analyze and identify potential relationships among data sets.

From the Inside Flap

Presents the methodology of big data analysis using examples from research and industry
There are large amounts of data everywhere, and the ability to pick out crucial information is increasingly important. Contrary to popular belief, not all information is useful; big data network analysis assumes that data are not only large but also meaningful, and this book focuses on the fundamental techniques required to extract essential information from vast data sets.

Featuring case studies drawn largely from the iron and steel industries, this book offers practical guidance that will enable readers to easily understand big data network analysis. Particular attention is paid to the methodology of network analysis, offering information on the method of data collection, on research design and analysis, and on the interpretation of results. A variety of programs including UCINET, NetMiner, R, NodeXL, and Gephi for network analysis are covered in detail.

Fundamentals of Big Data Network Analysis for Research and Industry looks at big data from a fresh perspective and provides a new approach to data analysis.

This book:
• Explains the basic concepts in understanding big data and filtering meaningful data
• Presents big data analysis within the networking perspective
• Features methodology applicable to research and industry
• Describes in detail the social relationship between big data and its implications
• Provides insight into identifying patterns and relationships between seemingly unrelated big data

Fundamentals of Big Data Network Analysis for Research and Industry will prove a valuable resource for analysts, research engineers, industrial engineers, marketing professionals, and any individuals dealing with accumulated large data whose interest is to analyze and identify potential relationships among data sets.

Edition details

• Publisher: Wiley
• Publication date: 2016
• Language: English
• ISBN 10: 1119015588
• ISBN 13: 9781119015581
• Binding: Hardcover
• Edition number: 1
• Number of pages: 216