Tech Mining

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.

Autorentext

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.

Zusammenfassung
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.

Inhalt
List of Figures. Preface.

Acknowledgments.

Acronyms & ShorthandsGlossary.

PART I. UNDERSTAND TECH MINING.

Chapter 1. Technological Innovation and the Need for Tech Mining.

1.1 Why Innovation is Significant.

1.2 Innovation Processes.

1.3 Innovation Institutions and Their Interests.

1.4 Innovators and Their Interests.

1.5 Technological Innovation in an Information Age.

1.6 Information About Emerging Technologies.

Chapter 1 Take-Home Messages.

Chapter Resources.

Chapter 2. How Tech Mining Works.

2.1 What is Tech Mining?

2.2 Why Do Tech Mining?

2.3 What Is Tech Mining's Ancestry?

2.4 How to Conduct the Tech Mining Process?

2.5 Who Does Tech Mining?

2.6 Where Is Tech Mining Most Needed?

Chapter 2 Take-Home Messages.

Chapter Resources.

Chapter 3. What Tech Mining Can Do for You.

3.1 Tech Mining Basics.

3.2 Tech Mining Analyses.

3.3 Putting Tech Mining Information to Good Use.

3.4 Managing and Measuring Tech Mining.

Chapter 3 Take-Home Messages.

Chapter 4. Example Results: Fuel Cells Tech Mining.

4.1 Overview of Fuel Cells.

4.2 Tech Mining Analyses.

4.3 Tech Mining Results.

4.4 Tech Mining Information Processes.

4.5 Tech Mining Information Products.

Chapter 4 Take-Home Messages.

Chapter Resources.

Chapter 5. What to Watch For in Tech Mining.

5.1 Better Basics.

5.2 Research Profiling and Other Perspectives on the Data.

5.3 More Informative Products.

5.4 Knowledge Discovery.

5.5 Knowledge Management.

5.6 New Tech Mining Markets.

5.7 Dangers.

Chapter 5 Take-Home Messages.

Chapter Resources.

PART II. DOING TECH MINING.

Chapter 6. Finding the Right Sources.

6.1 R&D Activity.

6.2 R&D Output Databases.

6.3 Determining the Best Sources.

6.4 Arranging Access to Databases.

Chapter 6 Take-Home Messages.

Chapter Resources.

Chapter 7. Forming the Right Query.

7.1 An Iterative Process.

7.2 Queries Based on Substantive Terms.

7.3 Nominal Queries.

7.4 Tactics and Strategies for Query Design.

7.5 Changing the Query.

Chapter 7 Take-Home Messages.

Chapter 8. Getting the Data.

8.1 Accessing Databases.

8.2 Search and Retrieval from a Database.

8.3 What to Do, and Not to Do.

Chapter 8 Take-Home Messages.

Chapter 9. Basic Analyses.

9.1 In the Beginning.

9.2 What You Can Do with the Data.

9.3 Relations Among Documents and Terms Occurring in Their Information Fields.

9.4 Relationships.

9.5 Helpful Basic Analyses.

Chapter 9 Take-Home Messages.

Chapter 10. Advanced Analyses.

10.1 Why Perform Advanced Analyses?

10.2 Data Representation.

10.3 Analytical Families.

Chapter 10 Take-Home Messages.

Chapter Resources.

Chapter 11. Trend Analyses.

11.1 Perspective.

11.2 An Example Time Series Description and Forecast.

11.3 Multiple Forecasts.

11.4 Research Fronts.

11.5 Novelty.

Chapter 11 Take-Home Messages.

Chapter Resources.

Chapter 12. Patent Analyses.

12.1 Why patent Analyses?

12.2 Getting Started.

12.3 The 'What' and 'Why' of patent Analysis.

12.4 Tech Mining Patent Analysis Case Illustration: Fuel Cells.

12.5 Patent Citation Analysis. ...