“Tech Mining” R&D Literature –
for Research Assessment &
Forecasting Innovation Pathways
Alan Porter
Search Technology, Inc.
&
Georgia Tech
alan.porter@isye.gatech.edu
404-384-6295
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The Data
Tech Mining
Research Assessment
Measures
Maps
Forecasting Innovation Pathways
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Mixed background
◦ B.S. in Chemical Engineering (Caltech)
◦ PhD in Engineering/Psychology (UCLA)
Research focus
◦ Technology intelligence, forecasting & assessment
Faculty – Georgia Tech (Prof Emeritus)
◦ Industrial & Systems Engineering
◦ Public Policy, and taught 10 years as well in
◦ Management (Management of Technology – “MOT”)
Small Business – Search Technology
◦ Decision aiding in complex environments since 1980
◦ Since 1994, develop & apply text mining software focusing
on Science, Technology & Innovation (ST&I)
Search Technology, 2012
#1: Papers Citing Level #2 Papers
– Citing Paper Overlay Maps
•Diffusion scores
[Knowledge Diffusion]
•Science Citing Overlay Maps
•Relative engagement by ISI
Subject Categories
#2: Main Level (e.g., research
outputs of a target program) –
publication overlay maps
•Integration scores (Average
diversity of areas of citation)
•Science citation maps
•Bibliographic coupling
Tracking multi-generational
research knowledge transfer
with
• Interdisciplinarity metrics
• Science overlay mapping
•“Specialization” scores (Diversity of areas
of publication)
•Science overlay maps (Location of
publications among ISI Subject Categories)
#3: Papers cited by #2
•Coherence measures (do #3
papers draw upon distinct
topics?)
•[ “Bibliographic Coupling”
measures available – e.g., %
shared references]
#4: Papers cited by #3
Web of Science (“WOS”)
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Indexes publications from ~12,000 leading journals
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Recently >1.5 million papers per year
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Includes several databases
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Science Citation Index Expanded (SCI)
Social Sciences Citation Index (SSCI)
Arts & Humanities Citation Index (A&HCI)
Conference Proceedings
Provides field-structured abstract records
◦ Classify journals into Subject Categories (“SCs”) –
presently, 224 for SCI + SSCI
◦ Provide Cited References for each paper – we apply thesauri
to associate to Cited SCs
◦ Separately search for Citing records for each paper to
discern Citing SCs
Case Examples
Case Examples
Search (Publications) Results
* Nominal search on “Alivisatos, A P” (one of the PIs)
* Not all are articles
* Co-author, year, institution information available to help filter
* Note Subject Areas = “SCs”
Cited Reference Search Results:
* Hypothetical search on “Kuhn, D” (not one of our PIs)
* Not just Kuhn, the education researcher
* Multiple citing articles (to be downloaded)
* Includes cites to non-WOS-indexed items (“Carn S Cogn”)
* Includes cites to co-authored items (…Kuhn)
Sample WOS Abstract Record (excerpted)
[Retrieved Publications and/or Citing Articles]
AU Oliver-Hoyo, M
Gerber, RW
TI From the research bench to the teaching laboratory: Gold nanoparticle layering
SO JOURNAL OF CHEMICAL EDUCATION
DT Article
C1 N Carolina State Univ, Dept Chem, Raleigh, NC 27695 USA.
AB …
CR BENTLEY AK, 2005, J CHEM EDUC, V82, P765
BOLSTAD DB, 2002, J CHEM EDUC, V79, P1101
HALE PS, 2005, J CHEM EDUC, V82, P775, …
NR 16
TC 1
PY 2007
VL 84
IS 7
BP 1174
EP 1176
SC Chemistry, Multidisciplinary; Education, Scientific Disciplines
Getting “SCs” = easy; Getting “Cited SCs” is more challenging
Case Examples
R&D Abstract Record Data Mining
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Extract available field information (authors,
affiliations, etc.)
“Text mine” to derive new field information:
“cited author,” “cited Subject Category,” etc.
Clean – i.e., Disambiguate -- authors, affiliations
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List Cleanup (fuzzy matching – e.g., almost the same)
Apply thesaurus (e.g., to combine variations)
Let’s take a look at the software:
Thomson Data Analyzer (VantagePoint)
But first, we introduce Tech Mining
QUESTIONS about R&D abstract records, etc.?
Tech Mining
Alan L. Porter and Scott W. Cunningham
John Wiley & Sons Inc., 2005
Search Technology, 2012
Search Technology, 2012
13 MOT Issues
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R&D Portfolio
Mgt
R&D Project
Initiation
Engr Project
Initiation
New Product
Development
Strategic
Planning
Track/forecast
emerging or
breakthrough
technologies
etc.
39 MOT Questions
~200 Innovation Indicators
What?
•
What’s hot?
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Fit into tech landscape?
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New frontiers at fringe?
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Drivers?
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Competing technologies?
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Likely development paths?
Who?
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Who are available experts?
•
Which universities or labs
lead?
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Mapping of topic clusters within
the technology
3-D trend charts for topic
clusters
Ratio of conference to journal
papers (benchmarked)
Scorecard rate-of-change
metrics for topic clusters
Time slices to show evolution of
topical emphases
Topic growth modeling (S-curve)
fit & extrapolation
Profile table of main players
Pie chart: Company vs.
Academic vs. Government
publishing
Spreading (or constricting) # of
players by topic
Search Technology, 2012
A. Fit
growth models to trend data to gauge
technology maturation.
B. Understand R&D processes within an
organization – key players, relationships &
C. Gauge commercialization timetable: Pie
Chart - % of R&D publications by industry vs.
academic vs. government.
D.Competitive/collaborative analysis -- compare
IPCs between companies (unique/common).
Search Technology, 2012
MANAGEMENT ACTIVITY
R&D portfolio selection
R&D project initiation
Engineering project initiation
New product development
New market development
Merger
Acquisition of intellectual property (IP)
Intellectual asset management
Open innovation
Competitive intelligence
Future technology opportunity analysis
Strategic technology planning
Technology roadmapping
RELEVANT INDICATOR EXAMPLE:
Geo-plot patent assignee concentration
Search Technology, 2012
MANAGEMENT ACTIVITY
R&D portfolio selection
R&D project initiation
Engineering project initiation
New product development
New market development
Merger
Acquisition of intellectual property (IP)
Intellectual asset management
Open innovation
Competitive intelligence
Future technology opportunity analysis
Strategic technology planning
Technology roadmapping
RELEVANT INDICATOR EXAMPLE:
Identify high% of publications by industry
compared to government and academics
Search Technology, 2012
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Technology Life Cycle Indicators
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Innovation Context Indicators
- e,g, growth curve location & projection
- e.g., presence or absence of success factors
(funding, standards, infrastructure, etc.)
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Product Value Chain and Market Prospects
Indicators
- e.g., applications, sectors engaged
Search Technology, 2012
Tech Mining Questions to Answer from fieldstructured data
Who?
Where?
What?
When?
How? & Why? – Need human analyst to interpret the data
Search Technology, 2012
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Spell out the Intelligence questions and how to
answer them
Get suitable data
Search (iterate) & retrieve ~abstract records
Import into text mining software (VantagePoint)
Clean the data
Analyze
Visualize (Map)
Integrate with Internet analyses & expert opinion
Summarize; Interpret; Communicate (multidimensionally)!
Standardize and semi-automate where possible
How does this fit with NRCC-KM efforts?
Search Technology, 2012
Technical Information
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ST&I databases (e.g.,
Web of Science;
Derwent World Patent
Index)
[field-structured data]
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Internet Sources
(e.g., Googling)
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Technical Expertise
Contextual Information
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Business, competition,
customer, financial,
or policy content
databases (e.g.,
Thomson One; Factiva)
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Internet Sources (e.g.,
blogs, website
profiling)
Business Expertise
Search Technology, 2012
On-line Data Sources
Cambridge Scientific Abstracts
Delphion
Dialog
EBSCOHost
Ei Engineering Village
Custom Data
Factiva
ISI Web Of Knowledge
Lexis Nexis
Micropatent
Ovid
Patbase
Questel-Orbit
SilverPlatter
STN
Thomson Innovation
Databases
Aerospace
Art Abstracts
Biobase
Biological Abstracts
Biological Sciences
Biosis
Biotechno
Business & Industry
CAPlus (AnaVist export)
Cassis
CBNB
Claims
Computer & Info Systems
Corrosion
Current Contents
Derwent Biotech Abstracts
Derwent Innovations Index
Derwent World Patent Index
Ei Compendex
EMBase
EnCompass Literature
EnCompass Patents
Energy
EnergySciTech
Engineering Materials Abstr
Envr Sci & Pollution Mgmt
ERIC
EuroPat
FamPat
Comma/tab delimited tables
Microsoft Excel and Access
SmartCharts
XML
Record/Field Tools
Focust
Food Sci & Tech
Foodline Market
Foodline Science
Forege
Frosti
FSTA
Gale PROMT
GeoRef
Global Reporter
IFIPAT
IFIUDB
INPADOC
INSPEC
IPA
ISD
ITRD
JAPIO
JICST
Kosmet
LGST
MATBUS
Medline
METADEX
Mgmt and Org Studies
Micropatent Materials
Mobility
NSF Awards
NTIS
Pascal
Patent Citation Index
PCT
PCTPAT
Phin
Pira
Pluspat
PROMT
PsycINFO
PubMed
Rapra
Recent Refs
Reference Manager
Science Citation Index
SciSearch
Scopus
Tech Research
ToxFile
Transport
USApps
USPat
Waternet
WaterResAbs
Web of Science
WeldaSearch
Wisdomain
Combine duplicate records
Remove duplicate records
Create “frankenrecords”
(merge records from
dissimilar sources)
Classify records
Merge fields
Clean up fields
Apply thesauri
A wealth of
diverse
information
sources for
innovation
management
VantagePoint Import Filters and Tools
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3.
A Newly Emerging Science & Technology
(NEST)
Combining technical intelligence from
multiple database analyses – to answer:
a. What? / When?
b. Who? / What?
Seeking to Forecast Innovation Pathways
a. Illustrating lots of Tech Mining tools
b. To be used selectively – focusing on the
target questions!
Search Technology, 2012
Georgia
Tech group has compiled
nanotechnology R&D records from several
databases
◦Modular, Boolean search (2006; update 2012)
One
area of “nano” focus – solar cells
Here, we spotlight Dye-Sensitized Solar Cells
(DSSCs) – work by Guo Ying & Ma Tingting with
Huang Lu, Doug Robinson, & others
◦Invented by O’Regan and Grätzel (1991)
◦Promising “3d Generation” solar cells
◦Commercialization still in its infancy
Striving
to track from research to innovation
[Forecasting Innovation Pathways]
Search Technology, 2012
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Search on your topic in a target database
Download to your computer
Use text mining software to help clean & analyze
Let’s take a look at the DSSC data in TDA software
Combination of search results from 2 databases
[Web of Science + EI Compendex]
6056 abstract records
[We’ll be showing “Research Assessment” results from
other data; then return to DSSCs to Forecast Innovation
Pathways]
Look to do:
Check fields
Cleaning the data
Basic analyses (lists of the content of a field;
matrices made of 2 lists)
Maps
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4.
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The Data
Tech Mining
Research Assessment
Measures
Maps
Forecasting Innovation Pathways
Azerbaijan’s
Research Profile
• Very basic research questions to demonstrate
country-level profiling
[see reference below for an in-depth country
profile]
• Who, what, where, when?
• How active is Azerbaijan?
 Changes recently?
• In what research areas?
• Leading research institutions?
Schoeneck, D.J., Porter,A.L., Kostoff, R.N., and Berger, E.M., Assessment
of Brazil’s research literature, Technology Analysis and Strategic
Management, 23 (6) 2011, 601-621.
Case Examples
When? Trend in Azerbaijan Publication in Journals
indexed by Web of Science
450
400
350
300
250
200
150
100
50
0
2005
2006
2007
2008
2009
Case Examples
Who?
Top among 993 Institutions
#
Author Affiliations
291 Issues to consider:
Baku State Univ
Natl Acad Sci Azerbaijan 254 A. Data cleaning
234
Azerbaijan Acad Sci
[combining name
Azerbaijan Natl Acad Sci 155
variations]
106 B. How to handle out-ofNatl Acad Sci
66
Russian Acad Sci
country institutions?
61
Azerbaijan Tech Univ
49
Azerbaijan State Oil Acad
42
Gazi Univ
35
Gebze Inst Technol
34
Yildiz Tech Univ
31
Azerbaijan Med Univ
27
Ankara Univ
22
Univ Rostock
20
Middle E Tech Univ
20
Tabriz Univ Med Sci
Examples
With Case
whom?
Top Collaborating Countries
Countries
Azerbaijan
Turkey
Russia
Iran
Germany
USA
England
Italy
Japan
Ukraine
Wales
Switzerland
France
Canada
Uzbekistan
#
1439
286
112
109
67
59
31
30
24
20
20
17
16
14
11
Examples
Who:Case
Funded
the research?
Funding Organization
INTAS
Russian Foundation for Basic Research
TUBITAK
Turkish State Planning Committee
Gazi University BAP
NATO
#
17
8
7
5
3
3
Case Examples
What Research Areas?
Macro-disciplines
Chemistry
Materials Sci
Engineering
Physics
Biomed Sci
Geosciences
Clinical Med
Computer Sci
Infectious Diseases
Agri Sci
Ecol Sci
Env Sci & Tech
Cognitive Sci
Health Issues
Policy Sciences
Psychology
Business & Mgt
Folklore
Language & Linguistics
Literature, British Isles
Social Studies
#
475
382
333
231
105
101
68
51
42
38
33
17
15
7
7
4
3
3
1
1
1
Macro-disciplines are based
on factor analysis of a year’s
worth of Web of Science (2007)
cross-journal citations
[thanks to Leydesdorff and Rafols]
Case Examples
Research
Profile: Azerbaijan 2005-09 by Disciplines (top 5)
Macro-Discipline Author Affiliations
Key Terms
Top 3
Top 5
Natl Acad Sci Azerbaijan [119] synthesis [72]
Baku State Univ [95]
thermodynamic properties
Azerbaijan Acad Sci [48]
[27]
Density [24]
Water [23]
methanol [21]
Materials Sci[382] Azerbaijan Acad Sci [95]
effect [29]
Baku State Univ [66]
TlInS2 [19]
Azerbaijan Natl Acad Sci [64] Incommensurate phase [17]
CRYSTALS [17]
SINGLE-CRYSTALS [14]
Engineering[333] Natl Acad Sci Azerbaijan [83] methanol [14]
Baku State Univ [74]
Initial stresses [11]
Azerbaijan Acad Sci [38]
sufficient conditions [10]
thermodynamic properties
[10]
approximation [10]
boundedness [10]
Physics[231]
Azerbaijan Acad Sci [58]
MODEL [22]
Baku State Univ [47]
PHYSICS [12]
Azerbaijan Natl Acad Sci [35] SCATTERING [10]
VARIABILITY [10]
SYSTEMS [9]
Biomed Sci[105] Baku State Univ [27]
EFFICIENCY [10]
Azerbaijan Med Univ [9]
sturgeons [8]
Azerbaijan Acad Sci [7]
diencephalon [7]
CYTOARCHITECTONIC
ANALYSIS [7]
Azerbaijan [7]
EXPRESSION [7]
organization [7]
Chemistry[475]
Authors
Year
Top 3
Abdulagatov, I M [25]
Magerramov, A M [19]
Chyragov, F M [18]
2008-09
48% of 475
Suleymanov, R A [16]
Altindal, S [14]
Tagiev, O B [13]
Mammadov, T S [13]
51% of 382
Akbarov, S D [22]
Guliyev, V S [16]
Khanmamedov, A K [9]
Abdulagatov, I M [9]
Nasibov, S M [9]
50% of 333
Shahverdiev, E M [13]
Shore, K A [13]
Aliev, T M [12]
Sultansoy, S [12]
51% of 231
Zeynalov, R [9]
Musayev, I [9]
Rustamov, E K [8]
Dadasheva, N [8]
39% of 105
221 SC Base Map – Sciences +
Social Sciences
Agri Sci
Ecol Sci
Infectious Diseases
Env Sci & Tech
Clinical Med
Geosciences
Biomed Sci
Chemistry
Cognitive Sci
Mtls Sci
Health & Social Issues
Engineering
Psychology
Physics
Business & MGT
Social Studies
Economics Politics & Geography
Computer Sci
Azerbaijan Research, 2005-09 on Global Map of Science, SCI-SSCI 2007
Env Sci & Tech
Agri Sci
Ecol Sci
Infectious
Diseases
Geosciences
Clinical Med
Chemistry
Mtls Sci
Engineering
Biomed Sci
Cognitive Sci.
Health & Social Issues
Psychology
Physics
Computer Sci.
Business & MGT
Social Studies
Econ. Polit. & Geography
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National Academies Keck Futures Initiative (15-year
program) to boost interdisciplinary research in the US
Measure interdisciplinarity for program evaluation
For a body of research
◦ Extract papers’ cited references
◦ Associate cited journals to Web of Science (WOS) Subject
Categories (SCs)
◦ Matrix of SC by SC interrelationships
◦ For given paper set, calculate
 “Integration” – breadth of SCs drawn upon
 “Specialization” – concentration of publication activity
 “Diffusion” – diversity of SCs citing the research
HSD vs Control
1.00
More
Disciplinary
0.90
0.80
Specialization by Project
0.70
0.60
HSD
0.50
Control Groups
0.40
0.30
0.20
More
Interdisciplinary
0.10
0.00
0.00
0.10
0.20
0.30
0.40
0.50
Integration by Project
0.60
0.70
0.80
0.90
Science mapping
 Research Network Mapping
[Social Network Analyses]
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◦ Co-authoring; co-citation; co-term; etc.
◦ Bibliographic coupling
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Geo-mapping
◦ For regional & cluster analyses
Thomson Data Analyzer Map Principles
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Nodes = entities mapped; larger implies more activity (but
relative to full data set, so differences among a relatively
homogeneous mapped set may not show up)
Multi-Dimensional Scaling (“MDS”) representations
◦ Closer proximity suggests stronger relationship (association)
◦ Accuracy is not guaranteed because of the dimensional
reduction from N-D to 2-D
◦ Position on X & Y axes has no inherent meaning
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Path-erasing Algorithm added to indicate relationship
◦ Heavier links (lines) indicate stronger relationship
◦ Absence of a link only means that relationship is less than
the arbitrary threshold selected
◦ In preparing maps, we vary threshold to show relationships
most effectively
Study research networks
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From publications
◦ Mainly compare: Before vs. After
◦ Secondarily, examine those deriving from NSF support
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From citations
◦ By researcher publications, or proposals
◦ To researcher publications
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For Target & Comparison Group researchers
Networks based on
◦ Social links [e.g., co-authoring]
◦ Intellectual links [e.g., cross-citing or bibliographic
coupling on SCs, topics, or whatever]
Co-citation Map
of the most cited
authors by
the 307
nano
social science
papers
[Use Auto-corr on
hi cited Authors]
Visions
Evolutionary Economics
NSF Research Assessments
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RCN (Research Coordination Networks) Program
◦ Can we see researcher network enrichment, Before to
After?
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HSD (Human & Social Dynamics) and CMG
(Collaborations in Math & Geosciences) Programs
◦ How interdisciplinary (compared to ~similar projects)?
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REESE (Research & Evaluation on Education in
Science & Engineering) Program
◦ How is Cognitive Science engaging with STEM
education, over time?
Topical Themes of Proposal
Reference Title Phrases
•Extract noun phrases using
Natural Language Processing
(NLP) in VantagePoint
•Consolidate term variations using
“fuzzy matching”
•Group like terms and build a
thesaurus for the area
•Could use to group proposals
•Can analyze emerging
research themes
•Can probe further to identify
who is active on what topics
[a factor map]
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by Ruimin Pei, CAS
Using Georgia Tech Web of Science (SCI)
nano dataset
Compare Multi-Institute Scientific
Organizations (“MISOs”):
◦ CAS (China)
◦ RAS (Russian Academy of Sciences)
◦ CNRS (France)
◦ CNR (Italy)
◦ CSIC (Spain
Coauthoring
among CAS
institutes
on nano
[partial
network
map]
CAS Grad
School
shows hi
centrality
ROLE/REESE Research Evaluation Targets
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3.
4.
Identify and Map the participating research
domains, over time
Elucidate the intellectual & social research
networks involved
Gauge how interdisciplinary the projects are
Look for impacts of the research support on
researchers’ emphases, productivity, and teaming
Fig. 7. RCN Project -- Researcher Collaboration:
Before vs. After NSF program funding
HSD Research Activities
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Key on the Year 2004 HSD awards (33 Projects; 28
with papers in WOS or Scopus)
Publications deriving from the awards
One interest: how much collaboration
◦ Within projects?
◦ Across projects?
Project
C
Project
A
Project
D
Project
H
Project
J
Project
F
Project
E
Project
G
Project
L
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N
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S
Project W
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HSD Co-authoring
Project
AA
Project
BB
Project
CC
Project
DD
Project EE
Project
C
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AA
Project M
Project
T
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Project W
Project
I
Project
Y
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Z
HSD Co-authoring
+Project
citing
DD
Project EE
Research Assessment
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Measures & maps
How much output?
Extent and nature of collaboration?
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2.
3.
4.
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The Data
Tech Mining
Research Assessment
Measures
Maps
Forecasting Innovation Pathways
Using multiple information resources
 in combination
 to Forecast Innovation Pathways (“FIP”)
 for New & Emerging Science & Technology
 to inform Technology Management
 Illustrating
via Nano-Dye Sensitized Solar
Cells - “DSSCs”
 Thanks to Guo Ying, Ma Tingting, and Huang
Lu, Beijing Institute of Technology, and Doug
Robinson, Nano-UK & University of Twente
Search Technology, 2010
10 Steps (non-linear!) to Forecast Innovation Pathways (FIP)
STAGE ONE
Step A: Characterize the technology’s nature
Understand the NEST and
its TDS (Technology
Step B: Model the TDS
Delivery System)
STAGE TWO
Tech Mine
Step C: Profile R&D
Step D: Profile innovation actors & activities
Step E: Determine potential applications
Step J: Engage experts
STAGE THREE
Step F: Lay out alternative innovation
Forecast likely innovation pathways
paths
Step G: Explore innovation components
Step H: Perform Technology Assessment
Step J: Engage experts
STAGE FOUR
Synthesize & report
Step I:
Synthesize and Report
Search Technology, 2012
Methods & Data Sources vis-à-vis Analytical Steps
Analytical Steps
A: Understand the NEST &
specify the driving questions
B: Model the TDS
C: Profile R&D
D: Identify key Actors
E: Identify Applications
F: Lay out alternative
innovation pathways
G: Explore innovation
elements required
H: Perform Technology
Assessments
I: Synthesize & report
J: Expert Checking
Step J.
Expert
checking
Bibliometric analyses
SCI &
Derwent
Factiva
Compendex
patents
business &
research
context
publications
data
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
~
10 Steps (non-linear!) to Forecast Innovation Pathways (FIP)
STAGE ONE
Understand the
technology and its
Technology Delivery
System (TDS)
STAGE TWO
Tech Mine
Step A: Characterize the technology’s nature
Step B: Model the TDS
Step C: Profile R&D
Step D: Profile innovation actors & activities
Step E: Determine potential applications
Step J: Engage experts
Step F: Lay out alternative innovation
STAGE THREE
Forecast likely innovation pathways
paths
Step G: Explore innovation components
Step H: Perform Technology Assessment
Step J: Engage experts
STAGE FOUR
Synthesize & report
Step I:
Synthesize and Report
Search Technology, 2012
Trends in Solar Cell Sub-technologies
Search Technology, 2012
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Simple, but effective “boxes and arrows” modeling
Focus on:
◦ What is needed to deliver a technology-enhanced
product (an innovation) to market?
[Technology Enterprise – depict along X axis]
◦ What external forces & influences need be
recognized and addressed?
[Contextual factors – depict off the X axis]
Identify key players and leverage points
Obtain reviews from multiple perspectives
Search Technology, 2012
Basic DSSC Technology Delivery System
What? [Potent Environmental Influences
on innovation prospects?]
Who? [Enterprise(s) to innovate?]
10 Steps (non-linear!) to Forecast Innovation Pathways (FIP)
STAGE ONE
Step A: Characterize the technology’s nature
Understand the technology
and its TDS (Technology
Step B: Model the TDS
Delivery System)
STAGE TWO
Step C: Profile R&D
Tech Mine
Step D: Profile innovation actors & activities
Step E: Determine potential applications
Step J: Engage experts
STAGE THREE
Forecast likely innovation
paths
Step F: Lay out alternative innovation pathways
Step G: Explore innovation components
Step H: Perform Technology Assessment
Step J: Engage experts
STAGE FOUR
Synthesize & report
Step I:
Synthesize and Report
Search Technology, 2012
Projecting Nano-enhanced Solar
Cell Research Activity
Actual data
Projected data
Search Technology, 2012
Search Technology, 2012
Databases
Leading DSSC Companies across
Samsung SDI Co LTD
Sharp Co Ltd
Nippon Oil Corp
Hayashibara Biochem Labs Inc
Fujikura Ltd
Chemicrea Co Ltd
Sumitomo Osaka Cement Co Ltd
Toshiba Co Ltd
Konarka Technologies Inc
DONG JIN SEMICHEM CO LTD
SONY CORP
Evonik Degussa GmbH
STMicroelectronics NV
Data Systems & Software Inc
Dongjin Semichem Co Ltd
Dyesol Ltd
SCI
EI
DWPI
Factiva
52*
27*
15*
14*
12*
10*
10*
9*
7*
0
10
0
0
0
0
3
38
24
35
9
8
8
3
7
11
1
10
0
0
0
1
3
65*
17*
27*
0
17*
0
3
2
11*
16*
17*
0
0
0
0
2
4
4
10*
0
9*
0
2
1
9*
8*
17*
15*
12*
8*
8*
8*
Search Technology, 2012



Who?
◦ ~19 or so patent families
◦ Samsung prominent (6)
Find out more – Profile Samsung
◦ 54 patent families
◦ ~2 inventor teams
◦ 1 team with 28 patents has all 6 of these
[network map next]
We could analyze their emphases – e.g., Manual
Code concentrations
◦ Discrete devices
◦ Electro-(in)organics
◦ Polymer applications, etc.
Search Technology, 2012
Samsung
Patent Analyses:
2 distinct inventor
teams -The upper team has
the 6 “glass wall”
related patents
Search Technology, 2012
Focused DSSC Cross-Charting:
Tracking Materials to Technology to Functions to Applications
Next steps: Consider ways to enhance key attributes; Consider “TDS” aspects;
Determine “Who” is active on particular elements.
10 Steps (non-linear!) to Forecast Innovation Pathways (FIP)
STAGE ONE
Step A:
Understand the NEST and
its TDS (Technology
Step B:
Delivery System)
STAGE TWO
Step C:
Tech Mine
Step D:
Characterize the technology’s nature
Model the TDS
Profile R&D
Profile innovation actors & activities
Step E: Determine potential applications
Step J: Engage experts
Step F: Lay out alternative innovation
STAGE THREE
Forecast likely innovation pathways
paths
Step G: Explore innovation components
Step H: Perform Technology Assessment
Step J: Engage experts
STAGE FOUR
Synthesize & report
Step I:
Synthesize and Report
Search Technology, 2012
Hunt for local experts willing to engage
 Key – faculty, but especially technical PhD
students
 Workshops

Search Technology, 2012
Envisioned
Application Areas
Niche
Conventional
Solar Cells
Goals
Anticipated potential
Product Platforms
Compound
Semiconductor
Film Solar Cells
Si - Film
Solar Cells
Functionalities
Expected to made
available
New film
deposition tech
reduces cost
Multiple exciton
generation (MEG)
Single-crystalline silicon
Advances in
Material R&D
Multi-crystalline silicon
Amorphous silicon
time
3D
Solar Cells
Large surface area
could help
charge separation
Quantum Dot
present
Quantum dot
Solar Cells
Dye sensitized
Solar Cells
Large surface
Area to increase
light absorption
Nanoparticle
Nanostructures that
are expected to be
applied to solar cells
PERSONAL
PRODUCTS
OFF GRID
GRID CONNECTED
Organic
Solar Cells
Tailor optical
properties through
its size
Nanowires
Carbon nanotubes
Cadmium sulfide (CdS)
Copper indium diselenide (CIS)
TiO2, ZnO
Organic Materials
Cadmium telluride (CdTe)
Short/Medium Term
Long Term
Well embedded
Envisioned
Application Areas
Niche markets
Niche
Conventional
Solar Cells
Anticipated potential
Product Platforms
Goals
Alignment
with market
needs?
GRID CONNECTED
Compound
Semiconductor
Film Solar Cells
Si - Film
Solar Cells
Functionalities
Expected to made
available
New film
deposition tech
reduces cost
Multiple exciton
generation (MEG)
Organic
Solar Cells
Tailor optical
properties through
its size
Nanowires
Scalability?
Nanomaterial
Regulation?
Carbon nanotubes
Single-crystalline silicon
Multi-crystalline silicon
Amorphous silicon
time
3D
Solar Cells
Large surface area
could help
charge separation
Quantum Dot
Advances in
Material R&D
Quantum dot
Solar Cells
Dye sensitized
Solar Cells
Large surface
Area to increase
light absorption
Nanoparticle
Nanostructures that
are expected to be
applied to solar cells
PERSONAL
PRODUCTS
OFF GRID
Cadmium sulfide (CdS)
Copper indium diselenide (CIS)
TiO2, ZnO
Organic Materials
Cadmium telluride (CdTe)
Search Technology, 2012
present
Short/Medium Term
Long Term
10 Steps (non-linear!) to Forecast Innovation Pathways (FIP)
STAGE ONE
Understand the NEST and
its TDS (Technology
Delivery System)
STAGE TWO
Tech Mine
Step A: Characterize the technology’s nature
Step B: Model the TDS
Step C: Profile R&D
Step D: Profile innovation actors & activities
Step E: Determine potential applications
Step J: Engage experts
STAGE THREE
Forecast likely innovation
paths
Step F: Lay out alternative innovation pathways
Step G: Explore innovation components
Step H: Perform Technology Assessment
Step J: Engage experts
STAGE FOUR
Synthesize & report
Step I:
Synthesize and Report
Search Technology, 2012
Research Assessment References



Porter, A.L., Newman, N.C., Myers, W., and Schoeneck,
D., Projects and Publications: Interesting Patterns in
U.S. Environmental Protection Agency Research,
Research Evaluation, Vol. 12, No. 3, 171-182, 2003.
Porter, A.L., Schoeneck, D.J., Roessner, D., and Garner, J.
(2010). Practical research proposal and publication
profiling, Research Evaluation, 19(1), 29-44.
Carley, S., and Porter, A.L., A forward diversity index,
Scientometrics, to appear -- DOI: 10.1007/s11192-0110528-1.
Science Maps
• Chen, C. (2003) Mapping Scientific Frontiers: The Quest for Knowledge Visualization,
Springer, London
• Boyack, K. W., Klavans, R. & Börner, K. (2005). Mapping the backbone of science.
Scientometrics, 64(3), 351-374.
• Leydesdorff, L. and Rafols, I. (2009) A Global Map of Science Based on the ISI
Subject Categories. Journal of the American Society for Information Science and
Technology, 60(2), 348-362.
• Boyack, K. W., Börner, K. & Klavans, R. (2009). Mapping the structure and evolution
of chemistry research. Scientometrics, 79(1), 45-60.
• Klavans, R. & Boyack, K. W. (2009). Toward a Consensus Map of Science. Journal of
the American Society for Information Science and Technology, 60(3), 455-476.
• Places & Spaces: http://www.scimaps.org/
Science Overlay Maps
• Rafols, I. & Leydesdorff, L. (2009). Content-based and Algorithmic Classifications of
Journals: Perspectives on the Dynamics of Scientific Communication and Indexer
Effects. Journal of the American Society for Information Science and Technology,
60(9), 1823-1835.
• Rafols, I., Porter, A.L., and Leydesdorff, L., (2010) Science overlay maps: A new tool
for research policy and library management, Journal of the American Society for
Information Science & Technology, 61 (9), 1871-1887, 2010.
• Rafols, I. and Meyer, M. (2009) Diversity and Network Coherence as indicators of
interdisciplinarity: case studies in bionanoscience. Scientometrics, 82(2), 263-287.
DOI 10.1007/s11192-009-0041-y.
• Porter, A.L., and Youtie, J., Where Does Nanotechnology Belong in the Map of
Science?, Nature-Nanotechnology, Vol. 4, 534-536, 2009.
• National Academies Keck Futures Initiative: //www.keckfutures.org
• National Academies Committee on Facilitating Interdisciplinary Research,
Committee on Science, Engineering and Public Policy (COSEPUP) (2005).
Facilitating interdisciplinary research. (National Academies Press, Washington,
DC).
• Klein, J. T. (1996), Crossing boundaries: Knowledge, disciplinarities, and
interdisciplinarities. (University Press of Virginia, Charlottesville, VA.).
• Porter, A.L., Cohen, A.S., Roessner, J.D., and Perreault, M. Measuring Researcher
Interdisciplinarity, Scientometrics, Vol. 72, No. 1, 2007, p. 117-147.
• Porter, A.L., Roessner, J.D., and Heberger, A.E., How Interdisciplinary is a Given
Body of Research?, Research Evaluation, Vol. 17, No. 4, 273-282, 2008.
• Porter, A.L., and Rafols, I. (2009), Is Science Becoming more Interdisciplinary?
Measuring and Mapping Six Research Fields over Time, Scientometrics, 81(3),
719-745.
• Rafols, I., and Meyer, M., Diversity and network coherence as indicators of
interdisciplinarity: case studies in bionanoscience, Scientometrics 82, 263-287,
2010.
• Stirling, A. (2007). A general framework for analysing diversity in science,
technology and society. Journal of The Royal Society Interface, 4(15), 707-719.
• Wagner, C.S., Roessner, J.D., Bobb, K., Klein, J.T., Boyack, K.W., Keyton, J.,
Rafols, I., and Borner, K. (2011), Approaches to understanding and measuring
interdisciplinary scientific research (IDR): A review of the literature, Journal of
Informetrics, 5(1), 14-26.

Porter, A.L., Guo, Y., Huang, L., and Robinson, D.K.R.,
Forecasting Innovation Pathways: The Case of Nanoenhanced Solar Cells, ITICTI - International
Conference on Technological Innovation and
Competitive Technical Intelligence, Beijing,


December, 2010.
Robinson, D.K.R., Huang, L., Guo, Y., and Porter, A.L.
(2013), Forecasting Innovation Pathways for New and
Emerging Science & Technologies, Technological
Forecasting & Social Change, 80 (2), 267-285.
Huang, L., Guo, Y., Zhu, D., Porter, A.L., Youtie, J.,
and Robinson, D.K.R., Organizing a Multidisciplinary
Workshop for Forecasting Innovation Pathways: The
Case of Nano-Enabled Biosensors, Atlanta Conference
on Science and Innovation Policy, 2011.
Search Technology, 2012







Porter, A.L., and Cunningham, S.W. (2005), Tech Mining: Exploiting New
Technologies for Competitive Advantage, Wiley, New York.
Porter, A.L. (2005), Tech Mining, Competitive Intelligence Magazine, 8
(1), 30-36.
Cunningham, S.W., Porter, A.L., and Newman, N.C. (2006), Tech Mining,
special issue of Technological Forecasting & Social Change, 73 (8), 9151060.
Porter, A.L. (2007), How ‘Tech Mining’ Can Enhance R&D Management,
Research Technology Management, 50 (2), 15-20.
Porter, A.L. (2009), Technology Monitoring – Tech Mining, in Ashton, W.B.
and Hohhof, B. (Eds.), Competitive Technical Intelligence, Competitive
Intelligence Foundation, Alexandria, VA., 125-129.
Porter, A.L., and Newman, N.C. (2011), Tech Mining Success Stories,
Technology Management Report, Center for Innovation Management
Studies (CIMS), Spring, 17-19.
Porter, A.L., Guo, Y., and Chiavetta, D. (to appear), Tech Mining: Text
mining and visualization tools, as applied to nano-enhanced solar cells,
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.
Search Technology, 2012
Resources

Text mining software like that used:
//ip.thomsonreuters.com/training/thomson-data-analyzer

Ongoing Research on Interdisciplinarity & to make your
own science overlay maps:
//idr.gatech.edu/ or www.leydesdorff.net/overlaytoolkit

Global Tech Mining Conference, in conjunction with the
Atlanta Conference on Science & Innovation Policy, 25-28
Sep., 2013, Atlanta
www.atlantaconference.org/

Global Tech Mining – forthcoming special issues of
Technological Forecasting & Social Change, and of
Technology Analysis & Strategic Management
Outtakes
1.
◦
◦
◦
2.
3.
◦
◦
Using multiple data resources for research
assessment
Publications – mainly via Web of Science
Citations – via Web of Science
Patents (not today)
Data cleaning and analyses
Using Thomson Data Analyzer (TDA) or VantagePoint
software
Visualization
Using VantagePoint together with Aduna, Pajek, Excel,
Gephi, etc.
Diversity:
‘attribute of a system whose elements may be
apportioned into categories’
Heuristics of
diversity
(Stirling, 1998; 2007)
(Rafols and Meyer, 2009)
Characteristics:
Variety: Number of distinctive categories
Balance: Evenness of the distribution
Disparity: Degree to which the categories
are different.
Variety
Shannon (Entropy): i pi ln pi
Herfindahl (concentration):
 i p i2
Dissimilarity:
Balance
Generalised Diversity (Stirling)
 i di
Disparity
ij(ij) (pipj)a (dij)b
[** Shannon &
Herfindahl
do not include
Disparity]
Bibliographic Coupling
Meta Overlay, HSD Citing
Env, Ag & Geo Sciences
Bio & Medical Sciences
Physical Sciences & Engr
Social & Behavioral Sciences