Weak signal detecting of industry convergence using information of products and services of global listed companies - focusing on growth engine industry in South Korea –

Kwon, Lee-Nam; Park, Jun-Hwan; Youngjoo, Moon; Lee, Bangrae; Shin, YoungHo; Kim, Young Kuk

doi:10.1186/s40852-018-0083-6

Cited by 16 publications

(7 citation statements)

References 16 publications

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“…To sum up, the study of the digital economy on the promotion mechanism of industrial integration can provide certain theoretical value for promoting industrial upgrading and optimizing economic structure. Industrial integration was first proposed in 1963, and technological innovation has gradually blurred industrial boundaries, which in turn has led to industrial integration [4][5][6]. After the concept of technology-based industrial integration was introduced by means of three overlapping circles (three circles representing computer industry, broadcasting industry, and printing industry), all circles began to pay extensive attention to industrial integration.…”

Section: Introductionmentioning

confidence: 99%

A Study of Industrial Convergence in the Context of Digital Economy Based on Scientific Computing Visualization

Zhou

Yang

Liao

2022

Mobile Information Systems

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With the rapid development of computer hardware and software and the rapid spread of network, the amount of data in scientific computing has exploded. Big data visualization has become one of the important research contents in scientific computing. Against the background of rapid development of information technology and digital technology, the scale of digital economy is increasing day by day, and China’s digital economy has ushered in unprecedented progress, and it has become an important force in leading economic development. The development of digital economy promotes industrial upgrading and transformation and facilitates industrial integration, and many traditional industries have given rise to a series of new industries. Scientific computational visualization is a new research discipline with a wide range of applications, including energy surveying, natural sciences, finance, and business. However, as a brand-new discipline, further research is needed in areas such as the application of scientific computational visualization techniques based on the digital economy environment. In this study, we propose a scientific computing visualization approach based on a big data framework for industry convergence analysis in the context of digital economy, which consists of a cloud computing platform for industry convergence in the context of digital economy by integrating neural networks in the platform, and scientific computing visualization by built-in GPU hardware. Traditional research methods for scientific computing visualization are discussed, and algorithms and techniques are classified in terms of the types of data domains studied. Finally, the further development and research directions of scientific computing visualization in the big data environment are given in conjunction with the main theoretical and application results achieved by our group in scientific computing for the field of big data visualization. Scientific computing visualization will not only promote the development and application of cutting-edge information technology, like cloud computing, big data, cyber security, artificial intelligence, computational science, system theory, and so on, but also bring a unique and disruptive development and change to the industrial integration in the context of digital economy.

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Section: Introductionmentioning

confidence: 99%

A Study of Industrial Convergence in the Context of Digital Economy Based on Scientific Computing Visualization

Zhou

Yang

Liao

2022

Mobile Information Systems

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“…One approach to predicting future signals is to harness intuitive judgment by experts; however, this is both time-consuming and costly [11,12]. Since the volume of textual data and influence of public opinion on social media are increasing both rapidly and continuously, there have been attempts to detect future signals using social media data [12] in various areas, including the use of solar cells [12], school bullying [13], and health and welfare policies [14].…”

Section: Introductionmentioning

confidence: 99%

A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology

Lee¹,

Park²,

Song³

2021

J Med Internet Res

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Background South Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends. Objective The aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues. Methods An ontology was developed using the Ontology Development 101 methodology. The domain and scope of the ontology were defined by compiling a list of competency questions. The terms were collected from Korean government reports, Korea’s Basic Plan for Low Fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on an ecological model. The internal structure of classes was defined using the entity-attribute-value model. The description logics of the ontology were evaluated using Protégé (version 5.5.0), and the content coverage was evaluated by comparing concepts extracted from social media posts with the list of ontology classes. The structural and representational layers of the ontology were evaluated by experts. Social media data were collected from 183 online channels between January 1, 2011, and June 30, 2015. To detect future signals of fertility issues, 2 classes of the ontology, the socioeconomic and cultural environment, and public policy, were identified as keywords. A keyword issue map was constructed, and the defined keywords were mapped to identify future signals. R software (version 3.5.2) was used to mine for future signals. Results A determinants-of-fertility ontology comprised 236 classes and terminology comprised 1464 synonyms of the 236 classes. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90% of the concepts that appeared in social media posts on fertility policies. Average scores for all of the criteria for structural and representations layers exceeded 4 on a 5-point scale. Violence and abuse (socioeconomic and cultural factor) and flexible working arrangement (fertility policy) were weak signals, suggesting that they could increase rapidly in the future. Conclusions The determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in this study will be useful for policy makers who are developing policy responses to low fertility.

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“…This field is one of the intensive investment sectors that was developed in response to climate change around the world [39]. The government has designated the sector as one of its national growth engines and is investing a large number of resources to ensure its competitiveness [40]. As a result, it is a key field for Korea's future as well as significant investments in the world being made, making it suitable for the analytical purposes of this study [41].…”

Section: Resultsmentioning

confidence: 99%

Identifying the Policy Direction of National R&D Programs Based on Data Envelopment Analysis and Diversity Index Approach

et al. 2021

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The Korean government has been continuously conducting diverse national R&D programs to discover new growth engines. The Republic of Korea is one of the countries with the largest investment in national R&D, but its efficiency was relatively low. In response, this study established a framework to identify the characteristics and direction of outstanding R&D programs. In this study, the performance of the R&D programs was identified in the sub-program unit. The efficiency of the national R&D program was analyzed using the data envelopment analysis model through the outputs of the national R&D programs such as papers and patents. However, patent and paper output would take time to be realized. Therefore, this study also calculated the diversity index of R&D programs to identify their potential expected performance. This study applied the suggested framework in the electric vehicle fields, which is one of the core growth engines of South Korea. A list of outstanding programs was identified from the National Institute of Science and Technology Information (NTIS) data. Additionally, this study also discovered the main technology areas and their current issues of outstanding and brand-new R&D programs. These results could contribute to suggesting the policy direction to conduct high-performance national R&D programs.

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Weak signal detecting of industry convergence using information of products and services of global listed companies - focusing on growth engine industry in South Korea –

Cited by 16 publications

References 16 publications

A Study of Industrial Convergence in the Context of Digital Economy Based on Scientific Computing Visualization

A Study of Industrial Convergence in the Context of Digital Economy Based on Scientific Computing Visualization

A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology

Identifying the Policy Direction of National R&D Programs Based on Data Envelopment Analysis and Diversity Index Approach

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