In this week’s reading, the concept of 3-F Method is introduced.  Discuss the purpose of this concept and how it is calculated.  Also perform your own research/analysis using these factors and provide your assessment on whether the United States need to introduce top talents in the field of big data and cloud computing by using bibliometrics. 

At least one scholarly source should be used in the initial discussion thread. Be sure to use information from your readings. Use proper citations and references in your post.

Analysis on the Demand of Top Talent Introduction

in Big Data and Cloud Computing Field in China

Based on 3-F Method
Zhao Linjia, Huang Yuanxi, Wang Yinqiu, Liu Jia

National Academy of Innovation Strategy, China Association for Science and Technology, Beijing, P.R.China

Abstract—Big data and cloud computing, which can help
China to implement innovation-driven development strategy and
promote industrial transformation and upgrading, is a new and
emerging industrial field in China. Educated, productive and
healthy workforces are necessary factor to develop big data and
cloud computing industry, especially top talents are essential.
Therefore, a three-step method named 3-F has been introduced
to help describing the distribution of top talents globally and
making decision whether they are needed in China. The 3-F
method relies on calculating the brain gain index to analysis the
top talent introduction demand of a country. Firstly, Focus on the
high-frequency keywords of a specific field by retrieving the
highly cited papers. Secondly, using those keywords to Find out
the top talents of this specific field in the Web of Science. Finally,
Figure out the brain gain index to estimate whether a country
need to introduce top talents of a specific field abroad. The result
showed that the brain gain index value of China’s big data and
cloud computing field was 2.61, which means China need to
introduce top talents abroad. Besides P. R. China, those top
talents mainly distributed in the United States, the United
Kingdom, Germany, Netherlands and France.

I. INTRODUCTION
Big data and cloud computing is a new and emerging

industrial field[1], and increasing widely used in China[2-4].
Talents’ experience is a source of technological mastery[5],
essentially for developing and using big data technologies.
Most European states consider the immigration of foreign
workers as an important factor to decelerate the decline of
national workforces[6]. Lots of universities and research
institutes have set up undergraduate and/or postgraduate
courses on data analytics for cultivating talents[7]. EMC
corporation think that vision, talent, and technology are
necessary elements to providing solutions to big data
management and analysis, insuring the big data success[8].

Bibliometrics research has appeared as early as 1917[9],
and has been proved an effective method for assessing or
identifying talents. Based on analyses of publication volume,
journals and their impact factors, most cited articles and
authors, preferred methods, and represented countries,
Gallardo-Gallardo et. al[10] assess whether talent management
should be approached as an embryonic, growth, or mature
phenomenon.

In this paper, we intend to analysis whether China need to
introduce top talents in the field of big data and cloud
computing by using bibliometrics. In section 2, the 3-F method

for top talent introduction demand analysis will be discussed.
In section 3, we will analysis the demand of top talent
introduction in big data and cloud computing field in China.

II. METHOD
In general, metering indicators contain the most productive

authors, journals, institutions, and countries, and the
collaboration networks between authors and institutions[11,
12]. Based on the commonly used bibliometrics method, 3-F
method for top talent introduction demand analysis is proposed.
3-F method has three steps:

Firstly, searching the literature database and forming a
high-impact literature collection in a specific field. Focusing on
the high-frequency keywords in the high-impact literature
collection by using the text analysis method as the research
hotspots. Just to be clear, the high-impact literature refers to the
journal literature whose number of cited papers ranked in the
top 1% in the same discipline and in the same year.

Secondly, retrieving those keywords in the Web of Science
to find out where those top talents of this specific field are.
Find the top talents by collected the information about talents’
country distribution, the institutions distribution and so on
through the high-impact literature collection. Among them, the
top talent refers to the first author or the communication author
of the high-impact literatures.

Finlly, Figure out the brain gain index to determine the top
talents introduction demand of a certain country. The brain gain
index is calculated as following formulas:

Iik = (Twk / Tik) / (Pw / Pi) (1)
Among them, Iik means the brain gain index value of

country (i) in the field (k), Twk means the number of world’s top
talents in the field (k), Tik means the number of country’s (i) top
talents in the field (k), Pw means the world population, Pi
means the country’s (i) population. If Iik was more than 1, that
means the country (i) has less top talents in the field (k),
therefore the talent introduction demand will be relatively
strong. In contrast, if Iik was less than 1, that means the
country’s (i) has greater top talents in the field (k) than the
world average, and the talent introduction demand will not be
so strong.

Additionally, the literature information mainly from the ISI
Web of Science (SCI, CPCI-S), and the the data analysis and
visualization tools are TDA and Tableau.

2017 Proceedings of PICMET ’17: Technology Management for Interconnected World

978-1-890843-36-6 ©2017 PICMET

III. CASE STUDY

Using 3-F method to analysis the top talents introduction
demand in the big data and cloud computing field. We
collected the high-impact literatures from January 1, 2006 to
July 31, 2016. The literature Language was English and the
literature type was article. Combining with the above
conditions, we got 546 high-impact literatures in the big data
and cloud computing field. Then the high-frequency keywords
have been obtained (Table 1) and served as the research
hotspots set.

TABLE I. THE RESEARCH HOTSPOTS OF THE HIGH-IMPACT LITERATURES IN
BIG DATA AND CLOUD COMPUTING FIELD

Order Keywords Frequency

1 cloud computing 48

2 big data 24

3 virtualization 11

4 cloud manufacturing 9

5 internet of things (IoT) 8

6 mobile cloud computing 8

7 bioinformatics 6

8 climate change 6

9 Hadoop 6

10 software-defined networking (SDN) 6

……

At the same time, we displayed the frequency distribution
of research hotspots in the way of cloud chart(fig. 1).

Fig. 1. The cloud chart of research hotspots that in the field of big data and

cloud computing

Then, we find the information about nationality (Table 2),
institutes (Table 3) of top talents in the high-impact literature
collection. Results showed there were 662 top talents
worldwide in the big data and cloud computing field. The top
ten countries or regions who had the most top talents were the
United States, P.R.China, the United Kindom, Germany, the
Netherlands, France, Canada, Australia, Italy and Switzerland
and Spain tied for the tenth.

TABLE II. THE NATIONALITY DISTRIBUTION OF TOP TALENTS IN THE BIG
DATA AND CLOUD COMPUTING FIELD

Order Country or Region Number of the top talent

1 US 268
2 P. R. China 48
3 UK 47
4 Germany 39
5 Netherlands 28
6 France 27
7 Canada 22
8 Australia 21
9 Italy 19
10 Switzerland 13
Spain 13
12 Japan 10
13 Korea 8
Malaysia 8
15 Singapore 7
New Zealand 7
17 Austria 6
18 Belgium 5
Sweden 5
India 5
Chinese Taipei 5
……

TABLE III. THE INSTITUTES DISTRIBUTION OF TOP TALENTS IN THE BIG
DATA AND CLOUD COMPUTING FIELD

Order Country or Region Number of the top talent

1 Harvard University (US) 10

2 Purdue University (US) 7

University of Malaya (Malaysia) 7

University of Maryland (US) 7

Unversity of Melbourne (Australia) 7

University of Missouri (US) 7

7 Oxford Unversity (UK) 6

8 Chinese Academy of Sciences (P.R.China) 5

ETH Zurich (Switzerland) 5

Massachusetts General Hospital (US) 5

Northwestern University (US) 5

University of British Columbia (Canada) 5

UC, Berkeley (US) 5

UC, San Diego (US) 5

University of Texas at Austin (US) 5

University of Washington (US) 5

……

2017 Proceedings of PICMET ’17: Technology Management for Interconnected World

From table 2 and 3 we can see that China was in the second
place worldwide. However, China’s top talent is much less than
the United States. In addition, the overall strength of Chinese
research institutions is not strong. So, whether China should
introduce top talents from other countries is need to be
discussed.

According to the formula of the brain gain index, and using
the world population data as well as the Chinese mainland
population data released by the World Bank, the value of the
Chinese brain gain index of big data and cloud computing was
2.61. In comparison, the brain gain index value of the United
States was 0.11. That means China need to introduce top talent
in the field of big data and cloud computing.

IV. CONCLUSION

In the knowledge economy era, the international flow of top
talent has become convenient and frequent. Facing the world’s
top talent shortage, China and the world’s major countries have
developed overseas top talent introduction programs. Until
2007, almost all European countries had introduced some
skillselective migration policies in order to attract the top
talents. To make the overseas top talent introduction programs
more effective and targeted is helpful for occupying the
strategic high ground in the global top talent competition.

This paper improved the traditional talent evaluation
function of bibliometric method, and presented the 3-F analysis
method, which was applied to analyze the demand of top
talents. The 3F method could help the government official to
make decision whether need to introduce top talents to develop
a new industry field and lock these top talents geographic
location.

REFERENCES
[1] .Xu, B.M., X.G. Ni. Development Trend and Key Technical Progress of

Cloud Computing[J]. Bulletin of the Chinese Academy of Sciences,
2015. 30(2), pp. 170-180.

[2] Xiao, Y., Y. Cheng, Y.J. Fang, Research on Cloud Computing and Its
Application in Big Data Processing of Railway Passenger Flow, in
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Management, P. Ren, Y. Li, and H. Song, Editors. 2015, Aidic Servizi
Srl: Milano. pp. 325-330.

[3] Zhu, Y.Q., P. Luo, Y.Y. Huo et. al, Study on Impact and Reform of Big
Data on Higher Education in China, in 2015 3rd International
Conference on Social Science and Humanity, G. Lee and Y. Wu,
Editors. 2015, Information Engineering Research Inst, USA: Newark. p.
155-161.

[4] Wang, X., L.C. Song, G.F. Wang et.al. Operational Climate Prediction
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[5] Dahlman, C., L. Westphal, Technological effort in industrial
development——An Interpretative Survey of Recent Research[R]. 1982.

[6] Cerna, L., M. Czaika, European Policies to Attract Talent: The Crisis
and Highly Skilled Migration Policy Changes, in High-Skill Migration
and Recession. 2016, Springer. pp. 22-43.

[7] Jin, X., B.W. Wah, X. Cheng et. al. Significance and challenges of big
data research[J]. Big Data Research, 2015. 2(2), pp. 59-64.

[8] Fang, H., Z. Zhang, C.J. Wang et. al. A survey of big data research[J].
IEEE Network, 2015. 29(5), pp. 6-9.

[9] Cole, F.J., Eales, N. B. The history of comparative anatomy[J]. science
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[10] Gallardo-Gallardo, E., S. Nijs, N. Dries et. al. Towards an understanding
of talent management as a phenomenon-driven field using bibliometric
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[11] Clarke, B.L. Multiple authorship trends in scientific papers[J]. Science,
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[12] Gonzalez-Valiente, C.L., J. Pacheco-Mendoza, R. Arencibia-Jorge. A
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Learned Publishing, 2016. 29(4), pp. 229-238.

2017 Proceedings of PICMET ’17: Technology Management for Interconnected World

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