• Smart Media Journal
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• v.8 no.1
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• pp.9-18
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• 2019

In the era of the 4th industrial revolution, the big data industry is gathering attention for new business models in the public and private sectors by utilizing various information collected through the internet and mobile. However, although the big data integration and analysis are performed with de-identification techniques, there is still a risk that personal privacy can be exposed. Recently, there are many studies to invent effective methods to maintain the value of data without disclosing personal information. In this paper, a personal information protection system is investigated to boost big data utilization in industrial sectors, such as healthcare and agriculture. The criteria for evaluating the de-identification adequacy of personal information and the protection scope of personal information should be differently applied for each industry. In the field of personal sensitive information-oriented healthcare sector, the minimum value of k-anonymity should be set to 5 or more, which is the average value of other industrial sectors. In agricultural sector, it suggests the inclusion of companion dogs or farmland information as sensitive information. Also, it is desirable to apply the demonstration steps to each region-specific industry.

• Journal of the East Asian Society of Dietary Life
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• v.23 no.4
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• pp.508-518
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• 2013

In this study, the influence of sub-factors of Big Five personality (extraversion, agreeableness, conscientiousness, neuroticism, openness to experience) to job satisfaction and turnover intention, and mediating effect of job satisfaction between Big Five personality and turnover intention were empirically analysed. Also the relationship between job satisfaction and turnover intention was investigated. The sample of this study were customer-contact employees of restaurant among Youngnam province. 243 questionnaires were used for the empirical analyses. According to the empirical analysis results, openness to experience and conscientiousness respectively had positive influences to job satisfaction and turnover intention. Also neuroticism gave negative influence to turnover intention. Another research finding of this study was that mediating effect of job satisfaction existed in the causal relationship of 'openness to experience' and conscientiousness to turnover intention. Managerial implications of the research findings are that restaurant managers are recommended to select employees of having much of conscientiousness and 'openness to experience', and to employ those of having much of agreeableness. In the field of restaurant industry, training is very important for employees to foster these personalities, because this industry does not have environment of hiring employees of having much of these personalities. Also training can be applied to employees of having high neuroticism for minimizing their turnover intention.

• Journal of People, Plants, and Environment
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• v.22 no.5
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• pp.455-466
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• 2019

This study was carried out to analyze trends in floral products and services through the big data analysis of various social networking services (SNSs) and then to provide objective marketing directions for the floricultural industry. To analyze the big data of SNSs, we used four analytical methods: Cotton Trend (Social Matrix), Naver Big Data Lab, Instagram Big Data Analysis, and YouTube Big Data Analysis. The results of the big data analysis showed that SNS users paid positive attention to flower one-day classes that can satisfy their needs for direct experiences. Consumers of floral products and services had their favorite designs in mind and purchased floral products very actively. The demand for flower items such as bouquets, wreaths, flower baskets, large bouquets, orchids, flower boxes, wedding bouquets, and potted plants was very high, and cut flowers such as roses, tulips, and freesia were most popular as of June 1, 2019. By gender of consumers, females (68%) purchased more flower products through SNSs than males (32%). Consumers preferred mobile devices (90%) for online access compared to personal computers (PCs; 10%) and frequently searched flower-related words from February to May for the past three years from 2016 to 2018. In the aspect of design, they preferred natural style to formal style. In conclusion, future marketing activities in the floricultural industry need to be focused on social networks based on the results of big data analysis of popular SNSs. Florists need to provide consumers with the floricultural products and services that meet the trends and to blend them with their own sensitivity. It is also needed to select SNS media suitable for each gender and age group and to apply effective marketing methods to each target.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.325-340
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• 2020

Today's rapidly developing health technology is accumulating vast amounts of data through medical devices based on the Internet of Things in addition to data generated in hospitals. The collected data is a raw material that can create a variety of values, but our society lacks legal and institutional mechanisms to support medical Big Data. Therefore, in this study, we looked at four major factors that hinder the use of medical Big Data to find ways to enhance use of the Big Data based healthcare industry, and also derived implications for expanding domestic medical Big Data by identifying foreign policies and technological trends. As a result of the study, it was concluded that it is necessary to improve the regulatory system that satisfies the security and usability of healthcare Big Data as well as establish Big Data governance. For this, it is proposed to refer to the Big Data De-identification Guidelines adopted by the United States and the United Kingdom to reorganize the regulatory system. In the future, it is expected that it will be necessary to have a study that has measures of the conclusions and implications of this study and to supplement the institutional needs to play a positive role in the use of medical Big Data.

• Journal of the Korean Society for Marine Environment & Energy
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• v.20 no.2
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• pp.117-125
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• 2017

SNS Big-data Analysis means to find potential value from big data which has produced by the social media. In this paper, SNS Big-data has been analysed to find Korean concerns by using 24 key words from the marine and fisheries sector. Among 24 key words, seafood, shipping and Dokdo Island are the most mentioned ones. Some key words such as ocean policies and marine security that have less concerns have bess mentioned less. Also, key words that are led by government are mostly mentioned by news media, but key words that are led by private sector and have intimate relationship with people's lives are mostly mentioned by Blogs and Twitters. Therefore, reflecting close national concerns by SNS Big-data Analysis and especially resolving negative factors are the most significant part of the policy establishment. Also, differentiated promotion methods need to be prepared because the frequency of key words mentioned from each type of media are different.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.693-696
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• 2013

Information protection system (Information protection system)-based IT environment built popularity in public agencies and businesses take advantage of the resources for the integration of the information system one essential environment began to recognize, cloud systems (Cloud System), cloud security (Cloud Security), big data (Big Data), big data security (Big Data Security), industrial security (Security Industry), as well as the issue. Due to the influence of these information protection system (Information protection system) in response to my external security threats based on the analysis plan. In this paper, data protection systems (Information protection system), resulting in the introduction, there are a number of security threats and particularly industrial security aspects and internal and external security threats in response by lighting about aspects of the plan is based on knowledge.

• Journal of the Korean Society of Clothing and Textiles
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• v.41 no.2
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• pp.378-391
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• 2017

The application of Big Data has been introduced to the Korean fashion industry; however, the literature has not yet investigated how well high technologies are being perceived and adopted by the practitioners of fashion companies. Recognizing the lack of research, the current research explores how big data analysis has been adopted by fashion practitioners based on the Technology Acceptance Model (TAM) that considers the effect of organizational characteristics (i.e., innovation, slack, and IS infra maturity). First, all TAM relationships were accepted as significant; however, the effect of perceived ease of use on the attitude toward big data was greater than perceived usefulness. Regarding organizational characteristics, while organization innovation had positive impacts on perceived usefulness as well as perceived ease of use, organization slack did not show significant and positive influence on perceived ease of use only. On the other hand, IS infra maturity had a negative effect on perceived usefulness while it did not have any significant impact on perceived ease of use. Finally, the level of perceived usefulness is decreasing as the IS infra of the fashion organization becomes more mature. With the results, the study suggested that fashion industry needs more education on the usage of big data analysis systems and development in related analysis tools.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.145-153
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• 2020

In the rapidly changing era of the fourth industry, big data is being used in various fields. In recent years, the use of big data has been rapidly applied to overall cultural and artistic contents, and among them, the use of big data is essential as a film genre with a lot of capital. This research method is analyzed as the film , which won the Palme d'Or Prize of the 72nd Cannes Film Festival in 2019 and the works and directors' award at the Academy Awards. The analyzed value predicts the film's performance through opinion mining, which gives the value of the change and sensitivity of each data cycle, and extracts the utilization index and topic keywords of SNS such as Facebook and Twitter to reflect the audience's interest. Identify the factors. As such, if model performance and model development can be predicted through model analysis of film performance using big data, the efficiency of the film production process will be maximized while the risk of production cost and the risk of film failure will be minimized.

• 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.

• The Journal of Fisheries Business Administration
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• v.48 no.1
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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.