• 한국CDE학회논문집
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• 제21권3호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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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.

• 유통과학연구
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• 제12권11호
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• pp.25-34
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• 2014

Purpose - Considering that the governments' official statistics on the optimum scale of the domestic service industry will be crucial in future, this study's results will be used as an important benchmark to develop and verify the parameters in the government's official statistics. Research design, data, and methodology - To identify the appropriate scale of Korea's service industry and its adequacy, I have determined them through estimation using a regression method involving panel data analysis on the panel data of 30 OECD countries. Results - The regression coefficient provided indications of being non-linear. This means that a U-shaped curve relationship exists-that is, the level of the economic growth leverage decreases along with the service industry's growth up to the level of 70.9% in terms of the Korean service industry's adequacy; it increases along with the service industry's growth at a level higher than 70.9%. Conclusions - While the current proportion of the size of the service industry among all industries in Korea stands at 50.7%, its proper proportion estimated by a regression analysis was 70.9%.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.43-49
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• 2024

In early 2020, as the COVID-19 pandemic hit the world, widespread changes occurred throughout society. COVID-19 also brought changes in consumers' consumption behaviors and preferences. This study aims to find out how the current status of the tourism industry and the coffee industry has changed since the end of COVID-19 by conducting big data analysis focusing on the search frequency of Naver, Google, and the following, which are representative social networks in Korea. Designating "Coffee Industry + Tourism Industry" as the representative keyword, January 1, 2020 to December 31, 2020, the time of each COVID-19 outbreak, was set before the COVID-19 type, and January 1, 2023 to December 31, 2023 was set after the end of COVID-19. Based on the analyzed search binder big data analysis within the period, we would like to find out how the current status of the tourism industry and the coffee industry has changed since the end of COVID-19. Finaly, the coffee and tourism industries are on the path of recovery and growth. In particular, the rise in coffee consumption, the recovery of the number of tourists, the emphasis on local tourism, and the strengthening of links with global markets are prominent.

• 조명전기설비학회논문지
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• 제25권4호
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• pp.88-96
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• 2011

This paper assesses interruption costs on the industry load through using the microscopic method. For assessment, the questionnaire was made on Korea Standard Industry Categorization which is composed of 28 type of industry Then, the survey was distributed to 1889 business in 12 area by staffs of KEPCO. The collected data is changed to the trustworthy data by using Bad Data Selection method and then the interruption costs of industry load was calculated by Tobit Regression which is tool analysing both collected data and the others.

• 수산경영론집
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• 제48권1호
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• pp.1-16
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• 2017

The fisheries industry has led the Korean economy, and has been achieving high-level position in the world. However, this industry meets aging, low growth and profit. In order to overcome this critical situation, it is needed to understand the overall status of industry. In industry level, most of previous researches focused on ocean industry rather than fisheries. In addition, scholars have been getting a lot of attention about fisheries cooperatives, fishing-ports, methods of fishery, and manufacturing process in fisheries sector. The aim of this research is analysis of domestic fisheries industry's managerial performance using data envelopment analysis(DEA) considering operating and scale view. Furthermore, the comparative analysis is performed by firm size, and industry type. In results, fisheries industry's managerial performance is not high, overall. In more detail, most of big size firms are under decreasing returns to scale(DRS) status. Fishery processing industry's performance is low, and fishery distribution industry has the best performance. This paper suggests that transferring operating capability from big firms to small firms, and policy supports and firm's activities should be accompanied for high-value added in fisher, and fishery processing industries.

• The Journal of Asian Finance, Economics and Business
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• 제8권12호
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• pp.337-343
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• 2021

The study's objective is to ascertain the state of the creative industry's market structure, the behavior of entrepreneurs ("conduct"), and the performance of the creative industry in Indonesia. Additionally, this study evaluates the relationship between structure, conduct, and performance within the context of the relationship between the three. This study analyzes longitudinal data from 2005 to 2015 for sub-sectors within the creative industry. The first step is to group statistical sub-sectors into creative-industry categories. The next step is to quantify and analyze the structure, behavior, and performance indicators of each creative industry subsector. Then, using a random effect panel data model, the relationship between structure and performance was estimated and examined. The findings of this study suggest that market share and concentration ratio calculations indicate that the creative industry in Indonesia has a monopolistic market structure. With this market framework, the creative industry's conduct can have an effect on prices. This is undoubtedly consistent with the features of the creative industry, which emphasize innovation as a means of adding value. The panel data estimation findings suggest the need for long-term efforts to maintain a market framework that enables businesses to compete fairly, innovate, and bring value.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.823-833
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• 2005

This paper deals the TFT-LCD process and quality, process control problems of the process. For improvement of the process quality and yield, we apply a data mining technique to the LCD industry. And some unique quality features of the LCD process are also described. We describe some preceding researches first and relate to the TFT-LCD process and the problems of data mining in the process. Also we tried to observe the problems which need to solve first and the features from description below hazard must be considered a quality mining in LCD industry.

• 한국IT서비스학회지
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• 제11권4호
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• pp.277-294
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• 2012

The purpose of this research is to analyze big data structure and various objects in big data industry based on ecological perspective. Big data is rapidly emerging as a highly valuable resource to secure competitiveness of enterprise and government. Accordingly, the main issues in big data are to find ways of creating economic value and solving various problems. However big data is not systematically organized, and hard to utilize as it constantly expands to related industry such as telecommunications, finance and manufacturing. Under this circumstance, it is crucial to understand range of big data industry and to which stakeholders are related. The ecological approach is useful to understand comprehensive industry structure. Therefore this study aims at confirming big data structure and finding issues from interaction among objects. Results of this study show main framework of big data ecosystem including relationship among object elements composing of the ecosystem. This study has significance as an initial study on big data ecosystem. The results of the study can be useful guidelines to the government for making systemized big data ecosystem and the entrepreneur who is considering launching big data business.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.831-838
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• 2022

Machine Learning is a process of using computer algorithms to extract information from raw data to solve complex problems in a data-rich environment. It has been used in the construction industry by both academics and practitioners for multiple applications to improve the construction process. The Construction Industry Institute, a leading construction research organization has twenty-five years of experience in benchmarking capital projects in the industry. The organization is at an advantage to develop useful machine learning applications because it possesses enormous real construction data. Its benchmarking programs have been actively used by owner and contractor companies today to assess their capital projects' performance. A credible benchmarking program requires statistically valid data without subjective interference in the program administration. In developing the next-generation benchmarking program, the Data Warehouse, the organization aims to use machine learning algorithms to minimize human effort and to enable rapid data ingestion from diverse sources with data validity and reliability. This research effort uses a focus group comprised of practitioners from the construction industry and data scientists from a variety of disciplines. The group collaborated to identify the machine learning requirements and potential applications in the program. Technical and domain experts worked to select appropriate algorithms to support the business objectives. This paper presents initial steps in a chain of what is expected to be numerous learning algorithms to support high-performance computing, a fully automated performance benchmarking system.