• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.1-7
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• 2023

Since the first outbreak of COVID-19 in 2019, it has caused a huge blow to the restaurant industry. However, as social distancing was lifted as of April 2022, the restaurant industry gradually recovered, and as a result, interest in restaurant start-ups increased. Therefore, in this paper, big data analysis was conducted by selecting "restaurant start-up" as a key keyword through social media big data analysis using Textom and then conducting word frequency and CONCOR analysis. The collection period of keywords was selected from May 1, 2022 to May 23, 2023, after the lifting of social distancing due to COVID-19, and based on the analysis, the development of a restaurant start-up consulting chatbot service is proposed.

• Journal of Korea Foundry Society
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• v.42 no.1
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• pp.35-41
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• 2022

In the present paper, the data management of casting processes has been discussed. In order to construct a smart factory in the foundry industry, understanding of the whole casting processes has to be in the first place. Casting process data can be obtained at the kiosk operated by casting engineers and data acquired by sensors in the foundry facility. However, preprocessing of the casting process data must be carried out in order to analyze the casting process by the data. Techniques and some examples for data preprocessing in the foundry was introduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3892-3912
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• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.23 no.2
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• pp.123-136
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• 2013

Objectives: This paper was prepapred to report airborne asbestos fiber concentrations in asbestos textile, brake-lining, commutator, and building materials manufacturing industries, and some other asbestos related industries in Korea from 1994 to 2006. Methods: Airborne asbestos data that have been sampled and analyzed in the above industries during 1994-2006 were collected. These data were reviewed to scrutinize the qualified data based on the records such as sampling and analyzed method and quality control procedures. All asbestos data were generated using the National Institute for Occupational Safety & Health (NIOSH) Method 7400. Results: Average concentration of asbestos fiber was 2.14 fibers/cc(0.02-15.6 fibers/cc) in the asbestos textile industry, 0.26 fibers/cc(0.01-1.01 fibers/cc) in the building-materials industry, 0.15 fibers/cc(0.01-0.93 fibers/cc) in the brake-lining manufacturing industry, and 0.14 fibers/cc(0.03-1.36 fibers/cc) in the commutator producing industry. For these industries, the percentage of samples of which asbestos fiber concentrations above the limit of exposure(0.1 fibers/cc) was 97.6% in the asbestos textile industry, 62.3% in the building-materials industry, 53.5% in the brake-lining manufacturing industry, and 34.3% in the commutator producing industry. Asbestos fiber concentration was below the limit of exposure in the gasket producing, petrochemistry, musical instrument producing industries, and the brake-lining exchange operations. Conclusions: Airborne asbestos fiber level in the asbestos textile, brake-lining producing, commutator and building-material producing industries was above the limit of exposure, but in the gasket producing, petrochemistry, musical instrument producing industries and the brake-lining exchange operations were below the limit of exposure.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.77-88
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• 2021

The development history of China's big data is relatively short, and it has only been ten years so far. Although the application level of big data in real life is not high, some achievements have been made in the supply chain. Various kinds of data will be generated in the actual operation of the supply chain. If these data can be effectively classified and used, the "bullwhip effect" of the operation of the supply chain can be also effectively improved. Thus this paper proposes the development of a supply chain collaborative inventory management model and application framework using big data. In this study, we analyzed the supply chain of beer industry, which is the most prominent consumption industry with "bullwhip effect", and further established a big data collaborative inventory management model for the supply chain of beer industry based on system dynamics. We used the Vensim software for simulation and sensitivity test and after appling our model, we found that the inventory fluctuations of the participants in the beer industry supply chain became significantly smaller, which verified the effectiveness of the model. Our study can be also applied to the possible problems of the large data supply chain collaborative inventory management model, and gives certain countermeasures and suggestions.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.25-37
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• 2022

The government and private companies are endeavoring to help the digital healthcare industry grow. This includes easing regulations on the big data industry such as the amendment of the Data 3 Act. Despite these efforts, however, there have been constant demands for the amendment of laws related to the medical field and for securing medical data transmissions. In this paper, the Data 3 Act of Korea and the legal system related to healthcare are examined. Then the legal, institutional, and technical aspects of the strategies are compared to understand the issues and implications. Based on this, a legal and institutional strategy suitable for the digital healthcare industry in Korea is suggested. Additionally, a direction to improve social perception along with technical measures such as safe de-identification processing and data transmission are also proposed. This study hopes to contribute to the spread of various convergent industries along with the digital healthcare industry.

• Convergence Security Journal
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• v.21 no.2
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• pp.19-28
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• 2021

Korea is promoting a digital new deal policy for the digital transformation and innovation accelerating of the industry. However, because of the strict existing data-related laws, there are still restrictions on the industry's use of data for the digital new deal policy. In order to solve this issue, a revised bill of the Data 3 Act has been proposed, but there is still insufficient discussion on how it will actually affect the activation of data use in the industry. Therefore, this study aims to analyze the perception of public opinion on the Data 3 Act and the implications of the revision of the Data 3 Act. To this end, the revision of the Data 3 Act and related research trends were analyzed, and the perception of the Data 3 Act was analyzed using a big data analysis technique. According to the analysis results, while promoting the vitalization of the data industry in line with the purpose of the revision, the Data 3 Act has a concern that it focuses on specific industries. The results of this study are meaningful in providing implications for future improvement plans by analyzing online perceptions of the industrial impact of the Data 3 Act in the early stages of implementation through big data analysis.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.113-122
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• 2016

Information and Communication Technology (ICT) has revolutionized the world and has profound impact on the social and economic development of a country. Implementation, practice and accessibility of ICT is viewed as an integral part of any countries' strategy today. These new technologies are becoming popular due to their ability to produce, distribute and provide instant access to massive information in no time. ICT has pervaded almost every aspect of human endeavor that may include health, education, economics, governance, entertainment etc. Tourism is one such vital industry that find enormous application of ICT in its strategic and operational level, to promise long term benefits and enhance economic growth. Tourism industry in western world and some developed countries of Asia have applied ICT for more than 30 years, and have gained tremendous benefits. Nepal which is also growing as one of the favourite tourist destinations lacks proper implementation of ICT in this industry. In our study we examined how the ICT can play a vital role in developing the tourism industry of Nepal. This study is an exploratory research based on primary data collected from tourist visiting Nepal, supported by information from tour operators, government agencies, NGOs and INGOS. A framework is devised on the basis of data and information collected and finally, discussions elaborate on the prospect and challenges of implementation of ICT in tourism industry of $Nep{\grave{a}}l$.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.151-160
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• 2021

The purposes of this research were: 1) to analyze the confirmatory factors with the business operational model of entrepreneurs of small and medium enterprises (SMEs) in the textile and clothing industry, and 2) to verify the congruence of the model with the operational ways of the entrepreneurs of SMEs in the textile and clothing industry. The sample consisted of 500 small and medium enterprise entrepreneurs in the textile and clothing industry. This study was quantitative research and the instrument used to collect the data was a questionnaire. The data was analyzed using 1^st order and 2^nd order of confirmatory analysis (CFA). The findings of this research revealed that the model of SMEs in the textile and clothing industry was overall at a high level. Four main factors were used for the model of SMEs in the textile and clothing industry by their importance in descending order as follows: marketing mix (MM), collaboration network (CN), production inventory management (PIM), and creativity (CT). The results of verification of model congruence revealed the model of SMEs in the textile and clothing industry was fit and in accordance with the empirical data.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.137-152
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• 2023

Data is indispensable for digital transformation of agriculture with the development of innovative information and communication technology (ICT). In order to devise and prioritize strategies for enhancing data competitiveness in the agricultural sector, we employed an Analytic Hierarchy Process (AHP) analysis. Drawing from existing research on data competitiveness indicators, we developed a three-tier decision-making structure reflecting unique characteristics of the agricultural sector such as farmers'awareness of the data industry or awareness of agriculture among data workers. AHP survey was administered to experts from both agricultural and non-agricultural sectors with a high understanding of data. The overall composite importance, derived from the respondents, was rated in the following order: 'Employment Support', 'Data Standardization', 'R&D Support', 'Start-up Ecosystem Support', 'Relaxation of Regulations', 'Legislation', and 'Data Analytics and Utilization Technology'. In the case of experts in the agricultural sector, 'Employment Support' was ranked as the top priorities, and 'Legislation', 'Undergrad and Grad Education', and 'In-house Training' were also regarded as highly important. On the other hand, experts in the non-agricultural sector perceived 'Data Standardization' and 'Relaxation of Regulations' as the top two priorities, and 'Data Center' and 'Open Public Data' were also highly rated.