Tech Mining Exploiting New Technologies for

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.

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.

List of Figures

xi

Preface

xiii

Acknowledgments

xv

Acronyms & Shorthands-Glossary

xvii

Part I. Understanding Tech Mining

1

(66)

1. Technological Innovation and the Need for Tech Mining

3

(14)

1.1. Why Innovation Is Significant

3

(2)

1.2. Innovation Processes

5

(3)

1.3. Innovation Institutions and Their Interests

8

(1)

1.4. Innovators and Their Interests

9

(3)

1.5. Technological Innovation in an Information Age

12

(1)

1.6. Information about Emerging Technologies

13

(2)

Chapter 1 Take-Home Messages

15

(1)

Chapter Resources

15

(2)

2. How Tech Mining Works

17

(16)

2.1. What Is Tech Mining?

17

(4)

2.2. Why Do Tech Mining?

21

(2)

2.3. What Is Tech Mining's Ancestry?

23

(1)

2.4. How To Conduct the Tech Mining Process?

24

(2)

2.5. Who Does Tech Mining?

26

(4)

2.6. Where Is Tech Mining Most Needed?

30

(1)

Chapter 2 Take-Home Messages

31

(1)

Chapter Resources

32

(1)

3. What Tech Mining Can Do for You

33

(8)

3.1. Tech Mining Basics

33

(1)

3.2. Tech Mining Analyses

34

(3)

3.3. Putting Tech Mining Information to Good Use

37

(1)

3.4. Managing and Measuring Tech Mining

38

(2)

Chapter 3 Take-Home Messages

40

(1)

4. Example Results: Fuel Cells Tech Mining

41

(10)

4.1. Overview of Fuel Cells

41

(1)

4.2. Tech Mining Analyses

42

(1)

4.3. Tech Mining Results

43

(3)

4.4. Tech Mining Information Processes

46

(2)

4.5. Tech Mining Information Products

48

(1)

Chapter 4 Take-Home Messages

49

(1)

Chapter 4 Resources

50

(1)

5. What to Watch for in Tech Mining

51

(16)

5.1. Better Basics

51

(5)

5.2. Research Profiling and Other Perspectives on the Data

56

(2)

5.3. More Informative Products

58

(1)

5.4. Knowledge Discovery

59

(3)

5.5. Knowledge Management

62

(1)

5.6. New Tech Mining Markets

63

(2)

5.7. Dangers

65

(1)

Chapter 5 Take-Home Messages

65

(1)

Chapter 5 Resources

66

(1)

Part II. Doing Tech Mining

67

(288)

6. Finding the Right Sources

69

(26)

6.1. R&D Activity

70

(3)

6.2. R&D Output Databases

73

(6)

6.3. Determining the Best Sources

79

(5)

6.4. Arranging Access to Databases

84

(9)

Chapter 6 Take-Home Messages

93

(1)

Chapter 6 Resources

94

(1)

7. Forming the Right Query

95

(18)

7.1. An Iterative Process

95

(1)

7.2. Queries Based on Substantive Terms

96

(5)

7.3. Nominal Queries

101

(3)

7.4. Tactics and Strategies for Query Design

104

(4)

7.5. Changing the Query

108

(3)

Chapter 7 Take-Home Messages

111

(2)

8. Getting the Data

113

(16)

8.1. Accessing Databases

113

(3)

8.2. Search and Retrieval from a Database

116

(9)

8.3. What to Do, and Not to Do

125

(2)

Chapter 8 Take-Home Messages

127

(2)

9. Basic Analyses

129

(26)

9.1. In the Beginning

129

(6)

9.2. What You Can Do with the Data

135

(2)

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

137

(4)

9.4. Relationships

141

(4)

9.5. Helpful Basic Analyses

145

(8)

Chapter 9 Take-Home Messages

153

(2)

10. Advanced Analyses

155

(32)

10.1. Why Perform Advanced Analyses?

155

(5)

10.2. Data Representation

160

(13)

10.3. Analytical Families

173

(8)

10.4 Debrand Trust Advanced Analysis Example

181

(4)

Chapter 10 Take-Home Messages

185

(1)

Chapter 10 Resources

186

(1)

11. Trend Analyses

187

(28)

11.1. Perspective

187

(4)

11.2. An Example Time Series Description and Forecast

191

(14)

11.3. Multiple Forecasts

205

(5)

11.4. Research Fronts

210

(2)

11.5. Novelty

212

(1)

Chapter 11 Take-Home Messages

213

(1)

Chapter 11 Resources

214

(1)

12. Patent Analyses

215

(34)

12.1. Basics

215

(3)

12.2. Why Patent Analyses?

218

(2)

12.3. Getting Started

220

(6)

12.4. The "What" and "Why" of Patent Analysis

226

(3)

12.5. Tech Mining Patent Analysis Case Illustration: Fuel Cells

229

(8)

12.6. Patent Citation Analysis

237

(2)

12.7. For Whom?

239

(4)

12.8. TRIZ

243

(3)

12.9. Reflections

246

(1)

Chapter 12 Take-Home Messages

247

(1)

Chapter Resources

248

(1)

13. Generating and Presenting Innovation Indicators

249

(40)

13.1. Expert Opinion in Tech Mining

250

(4)

13.2. Innovation Indicators

254

(20)

13.3. Information Representation and Packaging

274

(7)

13.4. Examples of Putting Tech Mining Information Representation to Use

281

(5)

13.5. Summing Up

286

(1)

Chapter 13 Take-Home Messages

287

(1)

Chapter 13 Resources

288

(1)

14. Managing the Tech Mining Process

289

(18)

14.1. Tough Challenges

289

(1)

14.2. Tech Mining Communities

290

(3)

14.3. Process Management

293

(3)

14.4. Enhancing the Prospects of Tech Mining Utilization

296

(3)

14.5. Institutionalizing the Tech Mining Function

299

(4)

14.6. The Learning Curve

303

(2)

Chapter 14 Take-Home Messages

305

(2)

15. Measuring Tech Mining Results

307

(14)

15.1. Why Measure?

307

(1)

15.2. What to Measure

308

(3)

15.3. How to Measure

311

(3)

15.4. Enabling Measurement

314

(2)

15.5. Effective Measurement

316

(2)

15.6. Using Measurements to Bolster Tech Mining

318

(1)

Chapter 15 Take-Home Messages

319

(1)

Chapter 15 Resources

320

(1)

16. Example Process: Tech Mining on Fuel Cells

321

(34)

16.1. Introduction

321

(1)

16.2. First Step: Issue Identification

322

(1)

16.3. Second Step: Selection of Information Sources

323

(1)

16.4. Third Step: Search Refinement and Data Retrieval

324

(1)

16.5. Fourth Step: Data Cleaning

324

(2)

16.6. Fifth Step: Basic Analyses

326

(13)

16.7. Sixth Step: Advanced Analyses

339

(12)

16.8. Seventh Step: Representation

351

(1)

16.9. Eighth Step: Interpretation

352

(1)

16.10. Ninth Step: Utilization

352

(1)

16.11. What Can We Learn

352

(1)

Chapter 16 Take-Home Messages

353

(1)

Chapter 16 Resources

354

(1)

Appendix

355

(14)

A. Selected Publication and Patent Databases

355

(6)

B. Text Mining Software

361

(2)

C. What You Can Do Without Tech Mining Software

363

(2)

D. Statistics and Distributions for Analyzing Text Entities

365

(4)

References

369

(8)

Index

377

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Tech Mining Exploiting New Technologies for Competitive Advantage

Summary

Author Biography

Table of Contents

Supplemental Materials