• Journal of Digital Convergence
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• v.13 no.10
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• pp.185-194
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• 2015

We are living in big data. Specially, female consumers are the hottest issue. Female consumers have a great effected on consumer culture as comparing male consumers. Therefore, this study analysis characteristics of female consumers through case study and literature review. The summarized results of research are as follows. First, percentage of economically active population of unmarried female of 20s is high, so they actively spend lots of money on buying goods and so on. Second, they are ahead of the curve and follow entertainers. Third, domestic case studies(SD online buz marketing, C.S.I. Shinsegaemall project, Service center only for female consumers of Shinhan Card, Travel Service of Lotte Tour) and international case studies(Big data service of Target, ZARA, and Walmart) show that if we utilize big data, we can raise re-purchasing desire and analysis needs of female consumers and create new female consumers.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.89-97
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• 2014

As Big Data's value is perceived and Government 3.0 is announced, there is a growing interest in Big Data. However, it won't be easy for each public institute or local government to apply Big Data systematically and make a successful achievement despite lacking of specific alternative plan or strategy. So, this study tried to suggest strategies to use Big Data after arranging the area which local government utilize it in. As a result, utilization areas of local administration's Big Data are divided into four areas; recognizing and corresponding the abnormal phenomenon, predicting and corresponding the close future, corresponding analyzed situation and developing new policy(administration service), and citizen customized service. In addition, strategies about how to use Big Data are suggested; stepwise approach, user's requirements analysis, critical success factors based implementation, pilot project, result evaluation, performance based incentive, building common infrastructure.

• Journal of the Korean Society for information Management
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• v.32 no.2
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• pp.63-86
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• 2015

Big data refers to dataset whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. And now it is considered to create the new opportunity in every industry. The purpose of this study is to develop of big data services in public library for improved library services. To this end, analysed the type of library big data and needs of stockholders through the various methods such as deep interview, focus group interview, questionnaire. At first step, we defined the 16 big data service models from interview with librarians, and LIS professions. Second step, it was considered necessity, timeliness, possibility of development. We developed the final two services called on 'Decision Support Services for Public Librarians' and 'Book Recommendation Services for Users.'

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.54-63
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• 2016

As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1141-1154
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• 2015

This paper describes the convergence of the energy technology and information and communication technology (ICT), which helps to consume less energy effectively. While a lot of researches have done against the increase of world energy usage, most of them focus on the efficiency of energy supply, transfer, and consumption equipment. Applying the ICT to decrease energy usage could help to find energy saving factors in the new field that has not been considered as a valuable one before. The big data service with the energy technology and ICT convergence enables correlation analyses of large sets of energy and environmental data. Finding a data tendency with a big data service helps to develop energy saving policies. Furthermore, it could make a further step to develop a new business model. This paper introduces the real cases of the company and project that provides a big data service with the ICT convergence.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.1
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• pp.148-158
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• 2018

This study analyzes recent trends in fashion retailing instigated by the fourth industrial revolution and approaches the trends in terms of the convergence of big data and artificial intelligence. The findings are as below. First, companies like 'Edited' and 'Stylumia' offer solutions that support the strategic decisions of fashion brands and fashion retailers by analyzing big data using artificial intelligence. Second, the convergence of big data and artificial intelligence scales personalized service on the web as examples of 'Coded Couture', 'StitchFix', and 'Thread'. Third, the insights gained from artificial intelligence and big data help create new fashion retailing platforms such as 'Botshop' and 'Lyst'. Last, artificial intelligence and big data assist with design. 'Ivyrevel' designs digital fashion, assisted by a macroscopic perspective on fashion trends, market and consumers through the analysis of big data. The Fourth Industrial Revolution brings changes across all industries that will likely accelerate. The fashion industry is also undergoing many changes with advancements in scientific technology. The convergence of big data and artificial intelligence will play a key role in the future of fast-moving industry like fashion, where fickle tastes of consumers are the main drivers.

• 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 Digital Convergence
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• v.12 no.8
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• pp.49-60
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• 2014

The primary purpose of this paper is to find out what issues are important in the Social Security sector, and then, through AHP methodology, this study analyzes what kind of big data methodologies and projects can be implemented to solves these issues. To the aim, this paper first confirmed 8 big data projects from reviewing all issues in the Social Security sector such as administrative works and social policies. After the result of pairwise comparison, policy validity is most important factors rather then effectiveness and practicability. With regard to the priorities among sub-big data projects, the project about preventing improper recipients has come out the most important project in terms of validity, effectiveness and practicability. And the results showed that the project about outreaching and reducing a blind spot on the welfare sector is weighed as a significant project. The results of this paper, in particular 8 sub-big data projects, will be useful to anyone who is interested in using big data and its methodologies for the social welfare sector.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3826-3851
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• 2016

With traditional data storage solutions becoming too expensive and cumbersome to support Big Data processing, enterprises are now starting to outsource their data requirements to third parties, such as cloud service providers. However, this outsourced initiative introduces a number of security and privacy concerns. In this paper, homomorphic encryption is suggested as a mechanism to protect the confidentiality and privacy of outsourced data, while at the same time allowing third parties to perform computation on encrypted data. This paper also discusses the challenges of Big Data processing protection and highlights its differences from traditional data protection. Existing works on homomorphic encryption are technically reviewed and compared in terms of their encryption scheme, homomorphism classification, algorithm design, noise management, and security assumption. Finally, this paper discusses the current implementation, challenges, and future direction towards a practical homomorphic encryption scheme for securing outsourced Big Data computation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.99-111
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• 2018

Service areas in expressways are very important facilities in terms of efficient expressway operation and the convenience of users. It needs a traffic management strategy to inform drivers in advance about congestion in service areas so as to distribute users of service areas. But due to the lack of sensors and data on numbers of people in the service areas, congestion in service areas had not been measured and managed appropriately. In this study, a congestion index for service areas was developed using telecommunication floating population big data. Two alternative indices (i.e., density of service areas and floating population V/c of service areas) were developed. Finally, the floating population V/c of service areas was selected as a congestion index for service areas for reasons of the ease of understanding and comparison.