• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.1
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• pp.89-97
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• 2014

Recently, spread of Smartphones caused activation of mobile services, because of that Big Data such as clouding service able to proceed with large amount of data which are hard to collect, save, search and analyze. Many companies collected variety of private and personal information without users' agreement for their business strategy and marketing. This situation raised social issues. As companies use Big Data, numbers of damage cases are growing. In this Thesis, when Big Data process, methods of analyze and research of data are very important. This thesis will suggest that choices of security levels and algorithms are important for security of private informations. To use Big Data, it has to encrypt the personal data to emphasize the importance of security level and selection of algorithm. Thesis will also suggest that research of utilization of Big Data and protection of private informations and making guidelines for users are require for security of private information and activation of Big Data industries.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.51-58
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• 2015

The utilization of Big data in the field of education has spread around the developed countries. However, in Korea, there are only experimental approaches related to Bigdata, yet for the related researches and services to appear. Therefore, it is the situation that needs to understand the reason for poor use of big data in the e-Learning industry, study and seek out alternatives to solve these problems. The result of this study shows that it was investigated that the high level of understanding of Bigdata has recognized large impact on e-Learning of Big Data and the more large-scale sales companies have recognized large impact on e-Learning of Big Data in the e-Learning industry. In conclusion, this study makes a proposal to expand the training and utilization policies of Bigdata relating to different sales scales.

• Convergence Security Journal
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• v.13 no.4
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• pp.69-75
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• 2013

Customer is the base factor of income for some corporations, so that effective CRM (Customer Relationship Management) is very important to develop the business. In order to use CRM efficiently, we should figure out customers' demands and provide services or products that the customers want. However, it is getting difficult to comprehend customers' demands because they have complicated form and getting more diverse. Recently, social media like Twitter and Facebook let customers to express their demands, and using big data is a very effective method for efficient CRM. This research suggests how to utilize big data for hotel CRM, which considers customer itself as asset of business. In addition, we discuss security problems of big data service and propose the solution for that.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.127-145
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• 2017

The purpose of this study is to empirical case study for the instructional design of flipped classroom by job-capability advancement of IT business majors. A student of IT business school has learned a lot of management educations for four years. But, they don't recognize a connection between school education and business practice. A subject based on the humanities, and social sciences consisted of mostly the memorization. The undergraduate class lack a practice's curriculum by a creative-oriented lesson rather than memorization-oriented. In particular, An IT business is now recognized as a significance emerging IT investment, the Internet of Things, information security, big data and strategy's ERP. For these reasons, it is important for an instructional design for understanding business practices of the students. Accordingly, Flipped classroom with participatory class be needed increasingly for students' practical sense. We will propose a design method of flipped classroom for inspiring business education. In this, new instructional design overturned traditional teaching method. After the student conducts a prior learn at home, school will accomplish a problem solving through question and answer. This design effected a boredom suppress and creative enforcement of student and an intimacy increase of instructor. In addition, A participatory class and reciprocal peer tutoring will be possible by a spontaneous self-directed learning of student. We were designed course of project type based on big data theory and application to target the fourth-year course. In conclusion, the new instruction provided a help to learning synergy between student and lecturer. During the lessons, the student showed improvement of business sense and enhanced problem solving capability. The lecturer has the intimacy through communication interaction with students.

• 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 Technology Services
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• v.11 no.4
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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.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.69
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• pp.617-632
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• 2016

After contracting of Korea-China FTA, we have expected the trade volume has mutually been increasing a lot. If we are using this contract, we would make an activation of Korea economy. Especially, Korea youngman has been caught a big chance to make a business in Korea & China. For example, Xian, which is one of center of silk road, has become big strategy area. As we need to focus on this area, we will be expected to indirect influences of consumer's industries like cosmetic, fishery products, food service, eco-friendly items. Recently, the youngman starting up a business has become a trend of kinds of alternative new job. Then, we have to provide practical solution for young man. One of this solution is 'starting up a business'. In this study, I empirically investigated the relation between Korea-China FTA and the youth starting up a business. So, I believe that this study can light up on the direction of effective starting up for making & success a business in China.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3331-3339
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• 2009

This study analyzed relation with performance on the job by strategy of outsourcing as study about outsourcing and system operation performance of mid-size ERP systems. To summarize the results of this study, first, mid-size ERP outsourcing companies in stages, rather than the big bang approach to introducing more effective measures could get the conclusion. Second, ERP outsourcing, the business strategies of the big bang as significant were analyzed in terms of partnership. This section introduces the methodology and business partners, regardless of outsourcing could be distinguished from the very important variables. As a result, the standard for business enterprises mid-size mad to keep a systematic business analysis is very difficult. Therefore, a gradual introduction of a step by step implementation of such a methodology capable of performing reliable business methods are needed.

• The Journal of Information Systems
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• v.30 no.1
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• pp.21-44
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• 2021

Purpose The opportunities and approaches to big data have grown in various ways in the digital era. Business analytics is nowadays an inevitable strategy for organizations to earn a competitive advantage in order to survive in the challenged environments. The purpose of this study is to analyze the intellectual structure of business analytics literature to have a better insight for the organizations to the field. Design/methodology/approach This research analyzed with the data extracted from the database Web of Science. Total of 427 documents and 23,760 references are inserted into the analysis program CiteSpace. Author co-citation analysis is used to analyze the intellectual structure of the business analytics. We performed clustering analysis, burst detection and timeline analysis with the data. Findings We identified seven sub- areas of business analytics field. The top four sub-areas are "Big Data Analytics Infrastructure", "Performance Management System", "Interactive Exploration", and "Supply Chain Management". We also identified the top 5 references with the strongest citation bursts including Trkman et al.(2010) and Davenport(2006). Through timeline analysis we interpret the clusters that are expected to be the trend subjects in the future. Lastly, limitation and further research suggestion are discussed as concluding remarks.