• Journal of Fashion Business
• /
• v.22 no.5
• /
• pp.32-40
• /
• 2018

Big breasted women often experience dissatisfaction with ready-made brassieres, that do not account for individual breast types nor provide adequate cup sizes. This study was conducted to provide basic information on common breast shape and measurements of Korean big-breasted women, and to facilitate development of big-breasted women's bras with excellent fit and comfort. The study analyzed direct upper body measurements of 178 women in their 20's whith cup size C or bigger in the 5th, 6th and 7th Size Korea. In addition, 3D body scan data of women with bra size 75 and cup size C were re-collected and their breast types were examined. Average under-bust circumference of big-breasted women was 75 size in brassiere size. The average stature was 159.78 cm and the body weight was 60.33kg, indicating "overweight". Also, it was revealed that common breast types of big-breasted women, were hemispheric and cone types. The study can facilitate better understanding of breast shapes and sizes of standard big-breasted women, and will be useful as reference in selection of subjects in future studies.

• Journal of Engineering Education Research
• /
• v.22 no.5
• /
• pp.37-48
• /
• 2019

The government has tried to develop a platform for systematically collecting and managing engineering education data for policy proposals. However, there have been few cases of big data analysis platform for policy proposals in engineering education, and it is difficult to determine the major function of the platform, the purpose of using big data, and the method of data collection. This study aims to collect the cases of big data analysis systems for the development of a big data system for educational policy proposals, and to conduct a study to analyze cases using the analysis frame of key elements to consider in developing a big data analysis platform. In order to analyze the case of big data system for engineering education policy proposals, 24 systems collecting and managing big data were selected. The analysis framework was developed based on literature reviews and the results of the case analysis were presented. The results of this study are expected to provide from macro-level such as what functions the platform should perform in developing a big data system and how to collect data, what analysis techniques should be adopted, and how to visualize the data analysis results.

• Journal of Smart Tourism
• /
• v.1 no.3
• /
• pp.3-5
• /
• 2021

The advancement of Information Communication Technology has provided tourism researchers with a golden opportunity to access big data, which plays a critical role in smart tourism. Recognizing the current issue, this paper discusses the evolution of the literature on tourism big data focusing on conceptual understanding of and types of big data, and insights from big data analytics. Indeed, this article provides important research agenda for future tourism researchers who would like to conduct academic research about big data and smart tourism.

• Management & Information Systems Review
• /
• v.33 no.2
• /
• pp.155-170
• /
• 2014

The purpose of this study is to inquire into the relationship between customer-employee exchange and organizational commitment. To achieve the purpose of this study, preliminary studies on customer-employee exchange, Big 5 character-types and organizational commitment after an overview of these variables were examined to design research models and set up research issues. To verify the research issues, a survey was carried out on employees at beauty shops located in Seoul, Gyeonggi, Busan and Ulsan areas. Questionnaires of collected 374 copies were used for a statistical analysis. The results of empirical analysis disclosed in this study are summarized as follows. First, customer-employee exchange had a positive effect on organizational commitment. Second, conscientiousness and openness of Big 5 character-types had a moderating effect on the relationship between customer-employee exchange and organizational commitment. But extraversion, neuroticism and agreeableness of Big 5 character-types had no moderating effect. The implications available through findings stated above are as follows. First, this study confirmed that good customer-employee exchange improves members' emotional commitment to organization. Second, in practical perspective, it may be effective to select employees with high openness and conscientiousness of character traits.

• Journal of Digital Convergence
• /
• v.12 no.4
• /
• pp.1-9
• /
• 2014

This research describes strategies to promote the growth of the Big Data industry and the companies within the ecosystem. In doing so, we identify the roles and responsibilities of various objects of this ecosystem and Big Data concepts. We describe the five components of the Big Data ecosystem: governance, data holders, service users, service providers and infrastructure providers. Related to the Big Data industry, the paper discusses 13 business strategies between the five components in the ecosystem. These strategies directly respond to areas of research by the Big Data industry leading experts on its early development. These strategies focus on how companies can gain competitive advantages in a growing new business environment of Big Data. The strategy topics are as follows: 1) the government's long term policy, 2) building Big Data support centers, 3) policy support and improving the legal system, 4) improving the Privacy Act, 5) increasing the understanding of Big Data, 6) Big Data support excavation projects, 7) professional manpower education, 8) infrastructure system support, 9) data distribution and leverage support, 10) data quality management, 11) business support services development, 12) technology research and excavation, 13) strengthening the foundation of Big Data technology. Of the proposed strategies, establishing supportive government policies is essential to the successful growth of thee Big Data industry. This study fosters a better understanding of the Big Data ecosystem and its potential to increases the competitive advantage of companies.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.5
• /
• pp.470-476
• /
• 2015

Big data has been used in diverse areas. For example, in computer science and sociology, there is a difference in their issues to approach big data, but they have same usage to analyze big data and imply the analysis result. So the meaningful analysis and implication of big data are needed in most areas. Statistics and machine learning provide various methods for big data analysis. In this paper, we study a process for big data analysis, and propose an efficient methodology of entire process from collecting big data to implying the result of big data analysis. In addition, patent documents have the characteristics of big data, we propose an approach to apply big data analysis to patent data, and imply the result of patent big data to build R&D strategy. To illustrate how to use our proposed methodology for real problem, we perform a case study using applied and registered patent documents retrieved from the patent databases in the world.

• The Korean Journal of Elementary Counseling
• /
• v.11 no.3
• /
• pp.371-390
• /
• 2012

This study examined the reliability and validity of the Korean short version of the Big-Five Questionnaire for children, a instrument designed to measure Big-Five personality domains of elementary school students. The short Big-Five Questionnaire for children was composed of 15 items based on exploratory factor analyses on th data from 5th and 6th grade elementary school students(N=278). Confirmatory factor analyses revealed evidence of structural validity of the Korean short version BFQ-C. The correlations of K-BFQC-SF with the criteria of depression, academic achievement, career maturity were assessed to verify criterion-related validity. The correlation coefficients were correspondent to the results of previous studies. This study is meaningful in that it is sufficient to assess five factor personality domains in school settings.

• Agribusiness and Information Management
• /
• v.5 no.2
• /
• pp.17-26
• /
• 2013

The purpose of this study is to prove that new values for the agricultural food area can be created by combining various big data collected in the agricultural food area and analyzing them in an appropriate analysis method. For this, the analysis techniques generally used were studied, and the use of the big data in the various areas of the current society was explored through practical application instances. In addition, by the current status and analysis instances of the big data use in the agricultural food area, this study was conducted to verify how the new values found were being used.

• Steel and Composite Structures
• /
• v.14 no.5
• /
• pp.431-451
• /
• 2013

The Big Bang-Big Crunch (BB-BC) optimization algorithm is developed for optimal design of non-linear steel frames with semi-rigid beam-to-column connections. The design algorithm obtains the minimum total cost which comprises total member plus connection costs by selecting suitable sections. Displacement and stress constraints together with the geometry constraints are imposed on the frame in the optimum design procedure. In addition, non-linear analyses considering the P-${\Delta}$ effects of beam-column members are performed during the optimization process. Three design examples with various types of connections are presented and the results show the efficiency of using semi-rigid connection models in comparing to rigid connections. The obtained optimum semi-rigid frames are more economical solutions and lead to more realistic predictions of response and strength of the structure.

• Journal of information and communication convergence engineering
• /
• v.17 no.4
• /
• pp.239-245
• /
• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.