This book begins with the premise that we have the information, the tools to exploit it, and the need for the resulting knowledge. Tech Mining makes exploitation of text databases meaningful to those who can gain from derived knowledge about emerging technologies. Tech Mining makes exploitation of text databases meaningful to those who can gain from derived knowledge about emerging technologies. It begins with the premise that we have the information, the tools to exploit it, and the need for the resulting knowledge. The information provided puts new capabilities at the hands of technology…mehr
This book begins with the premise that we have the information, the tools to exploit it, and the need for the resulting knowledge. Tech Mining makes exploitation of text databases meaningful to those who can gain from derived knowledge about emerging technologies.Tech Mining makes exploitation of text databases meaningful to those who can gain from derived knowledge about emerging technologies. It begins with the premise that we have the information, the tools to exploit it, and the need for the resulting knowledge. The information provided puts new capabilities at the hands of technology managers. Using the material present, these managers can identify and access the most valuable technology information resources (publications, patents, etc.); search, retrieve, and clean the information on topics of interest; and lower the costs and enhance the benefits of competitive technological intelligence operations.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
Produktdetails
Wiley Series in Systems Engineering and Management
ALAN L. PORTER's major concentration is technology intelligence, forecasting, and assessment. He has led the development of "technology opportunity analysis"-mining electronic bibliographic data sources to generate intelligence on emerging technologies. He holds a BS in chemical engineering from the California Institute of Technology, and a PhD in engineering psychology from UCLA. He is currently Director, Research & Development, for Search Technology, Inc., in Norcross, Georgia. SCOTT W. CUNNINGHAM worked for the Technology Policy & Assessment Center at the Georgia Institute of Technology. He has worked in industry as a data mining and machine learning consultant, working chiefly in the e-commerce and retail industries. He holds a BEng in engineering science and mechanics from Georgia Tech and a DPhil in science, technology, and innovation policy from the University of Sussex, UK. He currently serves as an assistant professor on the Technology, Policy, and Management faculty of TU Delft.
Inhaltsangabe
List of Figures. Preface. Acknowledgments. Acronyms & Shorthands--Glossary. PART I. UNDERSTAND TECH MINING. Chapter 1. Technological Innovation and the Need for Tech Mining. Chapter 2. How Tech Mining Works. Chapter 3. What Tech Mining Can Do for You. Chapter 4. Example Results: Fuel Cells Tech Mining. Chapter 5. What to Watch For in Tech Mining. PART II. DOING TECH MINING. Chapter 6. Finding the Right Sources. Chapter 7. Forming the Right Query. Chapter 8. Getting the Data. Chapter 9. Basic Analyses. Chapter 10. Advanced Analyses. Chapter 11. Trend Analyses. Chapter 12. Patent Analyses. Chapter 13. Generating and Presenting Innovation Indicators. Chapter 14. Managing the Tech Mining Process. Chapter 15. Measuring Tech Mining Results. Chapter 16. Examples Process: Tech Mining on Fuel Cells. Appendix A: Selected Publication and patent Databases. Appendix B: Text Mining Software. Appendix C: What You Can Do Without Tech Mining Software. Appendix D: Statistics and Distributions for Analyzing Text Entities. References. Index.
List of Figures. Preface. Acknowledgments. Acronyms & Shorthands--Glossary. PART I. UNDERSTAND TECH MINING. Chapter 1. Technological Innovation and the Need for Tech Mining. Chapter 2. How Tech Mining Works. Chapter 3. What Tech Mining Can Do for You. Chapter 4. Example Results: Fuel Cells Tech Mining. Chapter 5. What to Watch For in Tech Mining. PART II. DOING TECH MINING. Chapter 6. Finding the Right Sources. Chapter 7. Forming the Right Query. Chapter 8. Getting the Data. Chapter 9. Basic Analyses. Chapter 10. Advanced Analyses. Chapter 11. Trend Analyses. Chapter 12. Patent Analyses. Chapter 13. Generating and Presenting Innovation Indicators. Chapter 14. Managing the Tech Mining Process. Chapter 15. Measuring Tech Mining Results. Chapter 16. Examples Process: Tech Mining on Fuel Cells. Appendix A: Selected Publication and patent Databases. Appendix B: Text Mining Software. Appendix C: What You Can Do Without Tech Mining Software. Appendix D: Statistics and Distributions for Analyzing Text Entities. References. Index.
Rezensionen
"...useful to a variety of institutions, programs, and people." ( E-STREAMS , August 2005) "Two data mining practitioners explain how to use the available software tools...to quickly access the technological information needed to gain competitive advantage." ( Research Technology Management , May-June 2005)
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