• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.185-192
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• 2019

This study is to explore the relationship between the Fourth Industrial Revolution and the environment using the big data methodology. We scrutinize the trend of the Fourth Industrial revolution, in association with the environment, and provide implications for a more desirable future environmental policy. The results show that the Industrial Revolution has been generally perceived as negative to environment before the 2010s, while it has been widely regarded as positive after the period. It is highly expected that the Fourth Industrial Revolution will be capable of functioning as a new alternative to enhance the quality of the biophysical and social environment. This study justifies that the new wave of technological development may serve as a cure for the enhancement of the environmental quality. The positive linkage between the new technological development and the environment from this study clearly indicates that the environmental industry and environmental technologies will be key economic factors in the next-generation society. They should be of critical importance in shaping our cities into clearer and greener spaces, and people will continuously depend on the development of new environmental technologies in order to correct environmental damages.

• Electronics and Telecommunications Trends
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• v.35 no.1
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• pp.80-88
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• 2020

This paper describes the development trends and service provision examples of disaster occurrence and spread prediction technology for various disasters such as tsunamis, floods, and fires. In terms of fires, we introduce the WIFIRE system, which predicts the spread of large forest fires in the United States, and the Metro21: Smart Cities Institute project, which predicts the risk of building fires. This paper describes the development trends in tsunami prediction technology in the United States and Japan using artificial intelligence (AI) to predict the occurrence and size of tsunamis that cause great damage to coastal cities in Japan, Indonesia, and the United States. In addition, it introduces the NOAA big data platform built for natural disaster prediction, considering that the use of big data is very important for AI-based disaster prediction. In addition, Google's flood forecasting system, domestic and overseas earthquake early warning system development, and service delivery cases will be introduced.

• Journal of Advanced Research in Ocean Engineering
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• v.3 no.1
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• pp.32-40
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• 2017

Recently, the amount of data to be processed and the complexity thereof have been increasing due to the development of information and communication technology, and industry's interest in such big data is increasing day by day. In the shipbuilding and offshore industry also, there is growing interest in the effective utilization of data, since various and vast amounts of data are being generated in the process of design, production, and operation. In order to effectively utilize big data in the shipbuilding and offshore industry, it is necessary to store and process large amounts of data. In this study, it was considered efficient to apply Hadoop and R, which are mostly used in big data related research. Hadoop is a framework for storing and processing big data. It provides the Hadoop Distributed File System (HDFS) for storing big data, and the MapReduce function for processing. Meanwhile, R provides various data analysis techniques through the language and environment for statistical calculation and graphics. While Hadoop makes it is easy to handle big data, it is difficult to finely process data; and although R has advanced analysis capability, it is difficult to use to process large data. This study proposes a big data platform based on Hadoop for applications in the shipbuilding and offshore industry. The proposed platform includes the existing data of the shipyard, and makes it possible to manage and process the data. To check the applicability of the platform, it is applied to estimate the weights of offshore structure topsides. In this study, we store data of existing FPSOs in Hadoop-based Hortonworks Data Platform (HDP), and perform regression analysis using RHadoop. We evaluate the effectiveness of large data processing by RHadoop by comparing the results of regression analysis and the processing time, with the results of using the conventional weight estimation program.

• Journal of Convergence for Information Technology
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• v.8 no.5
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• pp.145-152
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• 2018

The paper aims to present the direction in which accounting education should proceed to enhance the expertise of accounting works in the new era in which big data is the center. This paper examines the definition and analysis of big data, and reviews the effectiveness through big data development in accounting expertise with specific references. Also, this paper presents some of the plans selected by professional accounting bodies and universities to address the topic of big data in the accounting curriculum. According to the plan, big data could provide a blueprint for the future role of accounting and financial experts. Therefore, what this study suggests is to improve educational content by adding big data topics to current accounting curricula in order to help accounting professionals of future generations prepare for technologies related to big data analysis in advance.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.341-347
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• 2014

Among methods of the big data process, big data process under the cloud environment is becoming a main topic. As part of solving faced problem and strengthening industrial competitiveness in the medical and health industry, discussion on ways to activate big data is actively being conducted. Because the reason is a paradigm shift, saving pressure for increasing health care costs, and increased consumer interest for the level of service. In this paper, we find out the relationship between the cloud and big data. And we are to research and analysis a cloud-based big data case in the medical field. Finally we propose the efficient utilization and future outlook. For the smooth functioning of cloud-based medical big data, we have to solve the problems like infrastructure extension, analysis/application software development, and professional manpower training. In addition, we have to correct insufficient laws maintenance to the Cloud utilization, and improve the security and the recognition to personal information, and solve authority for data centralization.

• Journal of Ginseng Research
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• v.6 no.1
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• pp.56-66
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• 1982

The effects of various dehiscent application such as dehiscent materials (big chaffs, vermiculite etc.), growth regulators and agricultural chemicals (plant protector.) on stimulation of dehiscence and shortening of dehiscent period were investigated Results obtained were as follows : 1. The moisture content of endosperm and seed coat at 10 day after dehiscent application amounts between 40% and 50%. 2. Endosperm diameter was increased with time of stratification, and the embryo growth showed in linear function, 3. Non-dehiscent seed showed also normally development of embryo, and the property of dehiscence dependent from physico-chemical nature of ginseng seed coat. 4. The best dehiscent materials were big chaffs and followed vermiculite, sand and sand with big chaffs. 5. The effect of dehiscence of ginseng seed showed higher activity in fungi than in bacteria in general. 6. Agricultural chemicals ( plant Protector) reduced the dehiscent rate of ginseng seed 7. The best timing of dehiscent treatment was between August 1 and August 10 but the smaller amount of dehiscent rate after August 10 dehiscent appllication indicated that big chaffs and growth regulator treatment may be controlled shortening of dehiscent period of ginseng seed.

• Knowledge Management Research
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• v.19 no.1
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• pp.119-132
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• 2018

Many advanced products and services are emerging in the market thanks to data-based technologies such as Internet (IoT), Big Data, and AI. The construction of a system for data processing under the IoT network environment is not simple in configuration, and has a lot of restrictions due to a high cost for constructing a high performance server environment. Therefore, in this paper, we will design a development environment for large data analysis computing platform using open source with low cost and practicality. Therefore, this study intends to implement a big data processing system using Raspberry Pi, an ultra-small PC environment, and open source API. This big data processing system includes building a portable server system, building a web server for web mining, developing Python IDE classes for crawling, and developing R Libraries for NLP and visualization. Through this research, we will develop a web environment that can control real-time data collection and analysis of web media in a mobile environment and present it as a curriculum for non-IT specialists.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.33-34
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• 2017

In 2015, Ministry of Health and Welfare of Korea announced a research and development plan of using Korean healthcare data to support decision making, reduce cost and enhance a better treatment. This project relies on the adoption of BigData technology such as Apache Hadoop, Apache Spark to store and process HealthCare Data from various institution. Here we present an approach a design and implementation of OLAP server in Korean HealthCare BigData platform. This approach is used to establish a basis for promoting personalized healthcare research for decision making, forecasting disease and developing customized diagnosis and treatment.

• Smart Media Journal
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• v.2 no.3
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• pp.47-53
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• 2013

Recently convergence of IT in logistics system as a typical application RFID/NFC technology is being used, such as, according to the distribution of the flow is generated by a lot of big data. The Hadoop distributed system to collect data items produced by the parallel processing capabilities of logistics information and logistics information for the record management can create. Hadoop system to support the design and development of prototypes were approaching the possibility of its utilization.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.6
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• pp.749-756
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• 2019

Big data is structured and unstructured data that is so difficult to collect, store, and so on due to the huge amount of data. Many institutions, including universities, are building student convergence systems to foster talents for data science and AI convergence, but there is an absolute lack of research on what kind of education is needed and what kind of education is required for students. Therefore, in this paper, after conducting the correlation analysis based on the questionnaire on basic surveys and courses to improve the curriculum by grasping the satisfaction and demands of the participants in the "2019 Big Data Youth Talent Training Course" held at K University, Regression analysis was performed. As a result of the study, the higher the satisfaction level, the satisfaction with class or job connection, and the self-development, the more positive the evaluation of program efficiency.