• Korean Journal of Human Ecology
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• v.15 no.4
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• pp.651-658
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• 2006

This study was performed to provide the analysis on their size and the structural factors required in the process of design and manufacture of school girls' shoes. 371 elementary school girls in Kyungin and Youngnam area were participated in the size measurement. 25 foot items and 6 main body items were measured directly or indirectly using a digital photography. The results of the study are as follows: first, by most of measured items, the range of their foot size was very wide from the size of toddlers to adults'. That shows that the change of school girls' foot size occurred with their growth is pretty big. Second, from the structural factor analysis on 25 foot items, five factors were extracted such as 'the size of the foot', 'the volume of the foot,' 'the height and inclination of the foot,' 'the shape of the foot,' and 'the inside and outside inclination of the foot'. Third, from the cluster analysis, three clusters were classified: Cluster 1 was the group of 10 to 11 year old girls who had big-sized feet. The elementary school girls in the fourth to sixth grade belonged to this group. Cluster 2 consisted of girls who had small-sized and big-volumed feet. Cluster 3 had medium-sized and slim-shaped feet. Most of 6 to 7 year old elementary school girls belonged to this group. The above-mentioned results imply that many continual researches are required on children's shoe production reflecting the change of elementary school girls' feet size owing to their growth. The quantitative data on elementary school girls' feet size in this study could be used as basic information for the development of children's shoe design and its production.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.4
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• pp.187-210
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• 2013

The consumption through e-journal consortia makes researchers locate and use academic resources and information extensively with comparatively cheap costs. This study analyzed and investigated substantive benefits of the big deal contracts for e-journal subscriptions in terms of efficient information use. In other words, this study compare concretely the differences in efficiency of using information between large-size institutions and small-size institutions who participate in the e-journal big deal contracts. This study suggests solutions for the problems which occur persistently and repeatedly in the big deal and new counter plans which can replace the current methods of big deal contracts in a long-term perspective by revealing the gaps of acquiring and using information by the size of participating institutions.

• Journal of Applied Reliability
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• v.18 no.3
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• pp.208-212
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• 2018

Purpose: The three Vs of volume, velocity and variety are commonly used to characterize different aspects of Big Data. Volume refers to the amount of data, variety refers to the number of types of data and velocity refers to the speed of data processing. According to these characteristics, the size of Big Data varies rapidly, some data buckets will contain outliers, and buckets might have different sizes. Correlation plays a big role in Big Data. We need something better than usual correlation measures. Methods: The correlation measures offered by traditional statistics are compared. And conditions to meet the characteristics of Big Data are suggested. Finally the correlation measure that satisfies the suggested conditions is recommended. Results: Mutual Information satisfies the suggested conditions. Conclusion: This article builds on traditional correlation measures to analyze the co-relation between two variables. The conditions for correlation measures to meet the characteristics of Big Data are suggested. The correlation measure that satisfies these conditions is recommended. It is Mutual Information.

• International Journal of Concrete Structures and Materials
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• v.5 no.1
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• pp.49-55
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• 2011

The main objective of this investigation is to study size effect on compressive strength of CFRP confined concrete cylinders subjected to axial compressive loading. In total 24 concrete cylinders with different sizes were tested, small specimens with a diameter of 100 mm and a height of 200 mm, medium specimens with a diameter of 200 mm and a height of 400 mm, and big specimens with a diameter of 300 mm and a height of 600 mm. The lateral confining pressure of each specimen is the same and from that hypothesis the small specimens were confined with one layer of CFRP, medium and big specimens were performed by two and three layers of CFRP respectively. Test results indicate a significant enhancement in compressive strength for all confined specimens, and moreover, the compressive strengths of small and medium specimens are almost the same while a bit lower for big specimens. These results permit to conclude that there is no size effect on compressive strength of confined specimens regardless of cylinder dimension.

• Journal of the Korean Ceramic Society
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• v.47 no.4
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• pp.308-311
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• 2010

Porous SiC samples were prepared with three types of wood (poplar, pine, big cone pine) by simply embedding the wood charcoal in a powder mixture of Si and $SiO_2$ at 1600 and $1700^{\circ}C$. The basic engineering properties such as density, porosity, pore size and distribution, and strength were characterized. The samples showed full conversion to mostly $\beta$-SiC with good retention of the cellular structure of the original wood. More rigid SiC struts were developed for $1700^{\circ}C$. They showed similar bulk density ($0.5{\sim}0.6\;g/cm^3$) and porosity (81~84%) irrespective of the type of wood. The poplar sample showed three pore sizes (1, 8, $60\;{\mu}m$) with a main size of $60\;{\mu}m$. The pine sample showed a single pore size ($20\;{\mu}m$). The big cone pine sample showed two pore sizes (10, $80\;{\mu}m$) with a main size of $10\;{\mu}m$. The bend strength was 2.5 MPa for poplar, 5.7 MPa for pine, 2.8 MPa for big cone pine, indicating higher strength with pine.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.567-582
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• 2014

The purpose of this study is to qualitatively identify the typologies and characteristics of the big data marketing strategy in major companies that are taking advantage of the big data business in Korea. Big data means piles accumulated from converging platforms such as computing infrastructures, smart devices, social networking and new media, and big data is also an analytic technique itself. Numerous enterprises have grown conscious that big data can be a most significant resource or capability since the issue of big data recently surfaced abruptly in Korea. Companies will be obliged to design their own implementing plans for big data marketing and to customize their own analytic skills in the new era of big data, which will fundamentally transform how businesses operate and how they engage with customers, suppliers, partners and employees. This research employed a Q-study, which is a methodology, model, and theory used in 'subjectivity' research to interpret professional panels' perceptions or opinions through in-depth interviews. This method includes a series of q-sorting analysis processes, proposing 40 stimuli statements (q-sample) compressed out of about 60 (q-population) and explaining the big data marketing model derived from in-depth interviews with 20 marketing managers who belong to major companies(q-sorters). As a result, this study makes fundamental contributions to proposing new findings and insights for small and medium-size enterprises (SMEs) and policy makers that need guidelines or direction for future big data business.

• Journal of applied mathematics & informatics
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• v.8 no.3
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• pp.959-973
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• 2001

We shall discuss one of some techniques needed to analyze algorithms. It is called a big-O function technique. The measures of efficiency of an algorithm have two cases. One is the time used by a computer to solve the problem using this algorithm when the input values are of a specified size. The other one is the amount of computer memory required to implement the algorithm when the input values are of a specified size. Mainly, we will restrict our attention to time complexity. To figure out the Time Complexity in nonlinear problems of Numerical Analysis seems to be almost impossible.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Journal of the Korean Institute of Rural Architecture
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• v.15 no.1
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• pp.107-113
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• 2013

The purpose of this study is to analyze the usage of the attached facilities of the church for the community services according to the church size. For the questionnaire 50 pasters who are in charge of the church that has over 300 church members were selected in Daejeon Chungnam Province. The results of this study were as follows; the kinds of attached facilities of the church do not increase by the church size, but in case of the middle size and the big size churches, the pressure of opening the attached facilities of the churches is stronger than that of small size churches. Almost all the pasters want to open the attached facilities of the churches for the community services, but the expectation of the result were different by the church size. Most of the pasters of the small size and big size churches think that the attached facilities of the church were very important for the growth of the church. Otherwise most of the pasters of the middle size churches do not agree with it.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.251-257
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• 2018

As the use of big data is necessary for increasing business value, the size of the big data market is getting bigger. Accordingly, it is important to apply competitive patents in order to gain the big data market. In this study, we conducted the patent analysis based keyword network to analyze the trend of big data patents. The analysis procedure consists of big data collection and preprocessing, network construction, and network analysis. The results of the study are as follows. Most of big data patents are related to data processing and analysis, and the keywords with high degree centrality and between centrality are "analysis", "process", "information", "data", "prediction", "server", "service", and "construction". we expect that the results of this study will offer useful information in applying big data patent.