Fundamentals of Big Data Network Analysis for Research and Industry
Nonfiction, Computers, Database Management
| Author: | Hyunjoung Lee, Il Sohn | ISBN: | 9781119015499 |
| Publisher: | Wiley | Publication: | November 16, 2015 |
| Imprint: | Wiley | Language: | English |
| Author: | Hyunjoung Lee, Il Sohn |
| ISBN: | 9781119015499 |
| Publisher: | Wiley |
| Publication: | November 16, 2015 |
| Imprint: | Wiley |
| Language: | English |
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.
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.
