• Journal of Web Engineering
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• v.20 no.3
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• pp.795-816
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• 2021

The existing industry evaluation method utilizes the method of collecting the structured information such as the financial information of the companies included in the relevant industry and deriving the industrial evaluation index through the statistical analysis model. This method takes a long time to calculate the structured data and cause the time delay problem. In this paper, to solve this time delay problem, we derive monthly industry-specific interest and likability as a time series data type, which is a new industry evaluation indicator based on unstructured data. In addition, we propose a method to predict the industrial risk index, which is used as an important factor in industrial evaluation, based on derived industry-specific interest and likability time series data.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.79-95
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• 2024

In modern society, as data plays a crucial role at the levels of businesses, industries, and nations, the utilization of data becomes increasingly important. Consequently, governments are prioritizing the development and implementation of plans to cultivate data workforce, viewing the data industry as a cornerstone of national strategy. To enhance domestic capabilities and nurture workforce in the data industry, it is deemed necessary to conduct an objective comparative analysis with major foreign countries. Therefore, this study aims to analyze cases of domestic and international data industries and explore methods for quantitatively comparing data industry workforce across nations. Initially, the study distinguishes between "data industry workforce" and "data job-related workforce," particularly focusing on professionals handling data-related tasks. Subsequently, it compares the workforce sizes of data job-related workforce across nations, utilizing standardized occupational classification codes based on the International Standard Classification of Occupations(ISCO). However, it should be noted that countries employing their own unique occupational classification systems often require matching job titles with similar meanings for accurate comparison. Through this study, it is anticipated that policymakers will be able to establish future directions for cultivating data workforce based on comparable status.

• 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.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.215-225
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• 2024

With the rapid development of Internet of Things (IoT) and big data technology, a large amount of data will be generated during the operation of related industries. How to classify the generated data accurately has become the core of research on data mining and processing in IoT industry chain. This study constructs a classification model of IoT industry chain based on improved random forest algorithm and text analysis, aiming to achieve efficient and accurate classification of IoT industry chain big data by improving traditional algorithms. The accuracy, precision, recall, and AUC value size of the traditional Random Forest algorithm and the algorithm used in the paper are compared on different datasets. The experimental results show that the algorithm model used in this paper has better performance on different datasets, and the accuracy and recall performance on four datasets are better than the traditional algorithm, and the accuracy performance on two datasets, P-I Diabetes and Loan Default, is better than the random forest model, and its final data classification results are better. Through the construction of this model, we can accurately classify the massive data generated in the IoT industry chain, thus providing more research value for the data mining and processing technology of the IoT industry chain.