• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.611-612
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• 2006

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.3
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• pp.483-497
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• 2023

This study aims to provide strategic recommendations for promoting the development of the global satellite data services industry by analyzing the startup landscape. Based on the analysis of startup data, such as number of startups, market segment, and funding amount, we examined the paradigm shift in the global satellite data services market, particularly its convergence with other market segments. To this end, we derived the cumulative funding-convergence dynamics matrix, which classifies the converging areas into four quadrants by considering the growth rate of converging segments and the cumulative funding amount. In this way, we can specify converging areas in the satellite data services market that bear potential importance for the creation of new markets. The findings of this study are expected to contribute to the advancement of the satellite data services industry and facilitate the exploration of new market opportunities. Furthermore, they can serve as a valuable reference for policy makers, industry stakeholders, government officials, and researchers involved in the satellite data services industry in capitalizing on the emerging space economy.

• Journal of Information Science Theory and Practice
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• v.9 no.3
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• pp.14-30
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• 2021

Data innovation is at the core of the Fourth Industrial Revolution. While the catastrophic COVID-19 pandemic has accelerated the societal shift toward a data-driven society, the direction of overall data regulation remains unclear and data policy experts have yet to reach a consensus. This study identifies and examines the ideal regulator models of data-policy experts and suggests an appropriate method for developing policy in the data economy. To identify different typologies of data regulation, this study used Q methodology with 42 data policy experts, including public officers, researchers, entrepreneurs, and professors, and additional focus group interviews (FGIs) with six data policy experts. Using a Q survey, this study discerns four types of data policy regulators: proactive activists, neutral conservatives, pro-protection idealists, and pro-protection pragmatists. Based on the results of the analysis and FGIs, this study suggests three practical policy implications for framing a nation's data policy. It also discusses possibilities for exploring diverse methods of data industry regulation, underscoring the value of identifying regulatory issues in the data industry from a social science perspective.

• East Asian Journal of Business Economics (EAJBE)
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• v.6 no.1
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• pp.31-41
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• 2018

Purpose - The main target to do this analysis is to find out the competitiveness between 2 countries (China and USA) in the aircraft business industry. The main target about mentioned research is to find out how a certain country takes more advantage against the other partner country in the country's trade structure. Research design, data, and methodology - Mentioned research period ranges from 1995 to 2016. Research basic data are coming from UN COMTRADE database which is top of top in the world statistical data and Research methods are used 3 types of international trade related theory for credible data outcomes. Results - Even though general data about aircraft industry are open to world society, detailed classified data are not easy to get them. Generally, Both China & USA are not easy to obtain data especially, in the overseas production field as a business secret which is one of research limitation in every research scopes. Conclusions - Even though Chinese aircraft industry looks like strong and more advantage against those of other countries based on competitive labor work wages and low price of raw material and resources, Actually, USA has overwhelmingly dominant advantage against that of China in the field of aircraft industry because USA has abundant capitals and up-to-date advanced high-technology as top of world economic communities. Additionally, even if USA aircraft industries hold a dominant position so far, if USA proposes sound competition relationship with China about aircraft industry, both 2 countries' future will be bright as their cooperation will make synergy effects for mutual benefits under current circumstances in 2 countries.

• The Korean Journal of Food & Health Convergence
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• v.10 no.1
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• pp.1-6
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• 2024

Purpose: The research aims to address the intricacies of AI and Big Data application within the food industry. This study explores the strategic implementation of AI and Big Data in the food industry. The study seeks to understand how these technologies can be employed to bolster consumer engagement and contribute to market expansion, while considering ethical implications. Research Method: This research employs a comprehensive approach, analyzing current trends, case studies, and existing academic literature. It focuses on the application of AI and Big Data in areas such as supply chain management, consumer behavior analysis, and personalized marketing strategies. Results: The study finds that AI and Big Data significantly enhance market analytics, consumer personalization, and market trend prediction. It highlights the potential of these technologies in creating more efficient supply chains, improving consumer satisfaction through personalization, and providing valuable market insights. Conclusion and Implications: The paper offers actionable insights and recommendations for the effective implementation of AI and Big Data strategies in the food industry. It emphasizes the need for ethical considerations, particularly in data privacy and the transparency of AI algorithms. The study also explores future trends, suggesting that AI and Big Data will continue to revolutionize the industry, emphasizing sustainability, efficiency, and consumer-centric practices.

• Journal of Information Technology Applications and Management
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• v.21 no.4_spc
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• pp.381-401
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• 2014

This study proposes the accurate economic effect (employment inducement coefficient, hiring inducement coefficient, index of the sensitivity of dispersion, index of the power of dispersion, and ratio of value added) of Korea software industry by analyzing the inter-industry relation using the modified inter-industry table. Some previous studies related to the inter-industry analysis were reviewed and the key problems were identified. First, in the current inter-industry table publishedby the Bank of Korea, the output of software industry includes not only the output of pure software industry (package software and IT services) but also the output of non-software industry due to the misclassification of the industry. This causes the output to become bigger than the actual output of the software industry. Second, during rewriting the inter-industry table, the output is changing. The inter-industry table is the table in the form of rows and columns, which records the transactions of goods and services among industries which are required to continue the activities of each industry. Accordingly, if only an output of a specific industry is changed, the reliability of the table would be degraded because the table is prepared based on the relations with other industries. This possibly causes the economic effect coefficient to degrade reliability, over or under estimated. This study tries to correct these problems to get the more accurate economic effect of the software industry. First, to get the output of the pure software section only, the data from the Korea Electronics Association(KEA) was used in the inter-industry table. Second, to prevent the difference in the outputs during rewriting the inter-industry table, the difference between the output in the current inter-industry table and the output from KEA data was identified and then it was defined as the non-software section output for the analysis. The following results were obtained: The pure software section's economic effect coefficient was lower than the coefficient of non-software section. It comes from differenceof data to Bank of Korea and KEA. This study hasa signification from accurate economic effect of Korea software industry.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.207-218
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• 2018

Recently, it becomes a big trend in the banking industry to apply a big data analytics technique to extract essential knowledge from their customer database. Such a trend is based on the capability to analyze the big data with powerful analytics software and recognize the value of big data analysis results. However, there exits still a need for more systematic theory and mechanism about how to adopt a big data analytics approach in the banking industry. Especially, there is no study proposing a practical case study in which big data analytics is successfully accomplished from the marketing perspective. Therefore, this study aims to analyze a target marketing case in the banking industry from the view of big data analytics. Target database is a big data in which about 3.5 million customers and their transaction records have been stored for 3 years. Practical implications are derived from the marketing perspective. We address detailed processes and related field test results. It proved critical for the big data analysts to consider a sense of Veracity and Value, in addition to traditional Big Data's 3V (Volume, Velocity, and Variety), so that more significant business meanings may be extracted from the big data results.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.326-331
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• 2023

Korea's cultural content industry can develop into another unique tourism industry. However, since other prior studies focus on the Japanese content industry, this study identifies modern industrial trends by combining the unique characteristics of Korean content, that is, cultural content tourism, and the analysis ability of big data. The current status and direction of the cultural content tourism industry were studied by utilizing the extensive information collection and in-depth analysis capabilities of big data, and as a result, it was confirmed that the trend of the cultural content industry is related to the business aspect of cultural content, not the pure content interest of cultural content. This shows that Korean cultural contents have a strong business aspect. As a limitation, when research design was conducted using social media big data, the age, gender, etc. of the subject analyzed with unique anonymity could not be known. The Korean cultural content industry is expected to be successful in terms of business.

• Korean Journal of Computational Design and Engineering
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• v.21 no.3
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• pp.334-340
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• 2016

As Information Technology (IT) is developed constantly, big data is becoming important in various industries, including ship and offshore industry where a lot of data are being generated. However, it is difficult to apply big data to ship and offshore industry because there is no generalized platform for its application. Therefore, this study presents a big data platform based on the Hadoop for applications in ship and offshore industry. The Hadoop is one of the most popular big data technologies. The presented platform includes existing data of shipyard and is possible to manage and process the data. To check the applicability of the platform, it is applied to estimate the weight of offshore plant topsides. The result shows that the platform can be one of alternatives to use effectively big data in ship and offshore industry.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.143-146
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• 2005

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.