Tech Mining Exploiting New Technologies for Competitive Advantage

by Porter, Alan L.; Cunningham, Scott W.

• ISBN13: 9780471475675
• ISBN10: 047147567X
• Edition: 1st
• Format: Hardcover
• Copyright: 2004-11-04
• Publisher: Wiley-Interscience

Summary

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.

Author Biography

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.

Table of Contents

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