=Paper=
{{Paper
|id=None
|storemode=property
|title=NEST: a Model for Detecting Weak Signals of Emerging Trends Using Global Monitoring Expert Network
|pdfUrl=https://ceur-ws.org/Vol-674/Paper114.pdf
|volume=Vol-674
|dblpUrl=https://dblp.org/rec/conf/ekaw/Kim10
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==NEST: a Model for Detecting Weak Signals of Emerging Trends Using Global Monitoring Expert Network==
https://ceur-ws.org/Vol-674/Paper114.pdf
NEST: A Model for Detecting Weak Signals of Emerging
Trends Using Global Monitoring Expert Network
Seonho Kim, Young il Kwon, Yong il Jeong, Sung-Bae Choi, Jong-Kyu Park, Sung-Wha Hong
Technology Information Analysis Lab.
Department of Information Analysis
Korea Institute of Science and Technology Information
66, Hoegi-ro, Dongdaemun-gu, Seoul 130-741, Korea
{haebang, ylkwn, yijeong, sbchoi, jkpark, shong}@kisti.re.kr
ABSTRACT research for modeling the analyzing and detecting processes has
The importance of analyzing R&D environment changes and not received any significant attention [5].
forecasting future technologies for supporting policy decision and NEST model utilizes knowledge of a group of experts from
efficient resource distribution has been increasingly recognized. various fields over the world and uses interactive feature of web
Many futurists are forecasting future technology based on Delphi 2.0 to communicate and deduce new refined knowledge from the
study, brainstorming, expert survey, trend analysis, data mining, shared knowledge.
etc. However, these processes still need to be formalized. In this
paper, we introduce the NEST (New & Emerging Signals of In the next section, the result of literature review on related
Trends) model, which is a systematic collective intelligence research is presented. In Section 3, a detailed description of
model for collecting information from expert network worldwide KISTI [4]‟s NEST model and its components is presented. In
and detecting weak signals of emerging technologies, developed Section 4, an experiment study performed to detect weak signals
by KISTI. The most outstanding feature of NEST model is that it and upward trends using the NEST model is provided. Then, we
is based on both quantitative and qualitative methods. In the conclude.
stages of quantitative methods, NEST performs clustering,
pattern recognition, scientometrics, and cross impact analysis. In 2. Related Work
the stages of qualitative methods, NEST conducts environmental
scanning, brainstorming, and Delphi study. For illustration 2.1 Weak Signal and Environment Scanning
purpose, a result of experiment for detecting weak signals of „Weak signal‟ is a small sign in present which has potential of
emerging technologies is presented. significant changes in the future. Environmental scanning is a
process of collecting and analyzing the environment information
Categories and Subject Descriptors of an organization or nation to support its decision making. Our
H.5.3 [Collaborative Computing]: Collaborative Method for NEST model performs environmental scanning using the GTB,
detecting Emerging Trends. Global Trends Briefing [1, 3].
Keywords 2.2 Trend Detection and Summarization
Hot topics, or trends, are detected by grouping documents into
Mass Knowledge, Weak Signal, Emerging Trend Detection,
concepts, based on a co-word or co-citation analysis.
Collaborative Computing.
3. NEST: New and Emerging Sign of Trend
1. INTRODUCTION
This paper introduces a collaborative environmental analysis 3.1 Global Monitoring and NEST-Clipping
model, NEST, for forecasting future trends useful to support The NEST consists of both quantitative analysis stages and
groups‟ or nations‟ decision making and R&D strategy qualitative analysis stages. The Global Monitoring, an
establishment. This model is designed to find weak signals of environmental scanning, in Step 1 is the first filtering of NEST
future trends. Weak signals are events, accidents, or strange process. This process is conducted in the manner of qualitative
issues that are thought to be the beginning of future changes [2]. analysis. In Step 2, the second filtering is performed on the
While the concept of weak signals begun to be discussed in collected information, both in qualitative and quantitative
strategic management literature already a quarter century ago, manner, by information analysts in KISTI based on its
and the importance of it has been widely perceived, the actual significance. In Step 3, various quantitative data analysis
techniques, such as clustering, pattern recognition, regression, Figure 2 shows an actual instance of the usage of weak signal
anomaly detection, etc. are used, to detect weak signals of trends, tracking board, obtained during the experiment study.
patterns and structures in the information. In Step 4, also a
quantitative analysis step, experts detect upward trends using the 3.3 Upward Trend Detection
weak signal tracking board, which is based on cross impact NEST‟s upward trend detection process is an application of auto-
analysis model developed by KISTI. regression based extrapolation model. A regular monitoring
NEST-Clipping is second filtering procedure on the monitored framework “Study-Watch board”, shown in Figure 3, is devised
information performed by information analysts based on its to detect trends and issues.
significance.
Figure 3: An example of Study-Watch board
4. Experiment and Conclusions
138 thousands of environmental scanning data collected by
Figure 1: Four steps of NEST model Global Monitoring Network, which has been operated for 10
years and archived in GTB website, is used as the source data.
57 weak signal candidates were selected after NEST-Signal
3.2 NEST-Signal Detection detection process, the step 3 in Figure 1, and prepared for online
In this step, information analysts analyze GTB and NEST-Clip to Delphi study. Table 2 presents the summary of the 57 weak
find the candidates of weak signal. An evaluation index for signal candidates.
measuring the strength of impact is defined by the information
analysts. Table 1 shows several examples of the index. Table 3: Number of prospective candidates for each field
5. REFERENCES
[1] Sung-Bae Choi, Young-Wook Park, Sung-Wha Hong and
Kyung-Ho Kim, A Study for the Link Service System of
Science & Technology Information, KISTI GTB Korea
Contents 2009, The Korea Contents Association, Busan,
Korea, May 2009, 731-736.
[2] Elina Hiltunen, The Future Sign and Its Three Dimensions.
Figure 2: An example of Weak Signal Tracking Board
In Futures, 40 (3), April 2008, 247-260.
[3] KISTI, GTB, Global Trends Briefing, available at
Table 1: Example of impact strength index http://radar.ndsl.kr/, 2010.
- Range of influence: local national International
[4] KISTI, Korea Institute of Science and Technology
- Growth of industry: primary secondary tertiary
- Degree of Convergence: stage 1 stage 2 stage 3
Information, available at http://www.kisti.re.kr/english/,
- Stage of R&D life cycle: Planning Science Development (6 sub- 2010.
stages) Industrialization
- Extension of social system: case regulation treaty rule law
[5] Alan L. Porter and Scott W. Cunningham, Tech Mining:
Exploiting New Technologies for Competitive Advantage.
- Increase of occurrence frequency: very weak weak neutral
Wiley-InterScience, 2005.