• Journal of Gifted/Talented Education
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• v.13 no.1
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• pp.43-63
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• 2003

The purpose of this study is to investigate the relationships between the thinking styles and the big five personality traits of gifted students. Two hundred and fifty-five gifted students(169 boys, 97 girls) enrolled in the Science Elite Program responded to the Big Five Personality Inventory and Thinking Styles Inventory. Although significant relationships were identified between particular thinking styles and certain personality traits, it was concluded that it is premature to claim that a personality measure can be used to measures thinking styles. Neuroticism, Agreeableness, in Big Five Personality Inventory and level and form dimensions of Thinking Styles Inventory was found to measure the each construct independently.

• Korean Journal of Microbiology
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• v.55 no.3
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• pp.268-273
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• 2019

The intestinal microbiomes vary according to the factors such environment, age and diet. The purpose of this study was to compare the gut microbial diversity between Korean infants receiving breast-fed milk and formula-fed milk. We analyzed microbial communities in stool samples collected from 80 Korean infants using next generation sequencing. Phylum level analysis revealed that microbial communities in both breast-fed infants group (BIG) was dominated by Actinobacteria ($74.22{\pm}3.48%$). Interestingly, the phylum Actinobacteria was dominant in formula-fed infants group A (FIG-A) at $73.46{\pm}4.12%$, but the proportions of phylum Actinobacteria were lower in formulafed infants group B and C (FIG-B and FIG-C) at $66.52{\pm}5.80%$ and $68.88{\pm}4.33%$. The most abundant genus in the BIG, FIG-A, FIG-B, and FIG-C was Bifidobacterium, comprising $73.09{\pm}2.31%$, $72.25{\pm}4.93%$, $63.81{\pm}6.05%$, and $67.42{\pm}5.36%$ of the total bacteria. Furthermore, the dominant bifidobacterial species detected in BIG and FIG-A was Bifidobacterium longum at $68.77{\pm}6.07%$ and $66.85{\pm}4.99%$ of the total bacteria. In contrast, the proportions of B. longum of FIG-B and FIG-C were $58.94{\pm}6.20%$ and $61.86{\pm}5.31%$ of the total bacteria. FIG-A showed a community similar to BIG, which may be due to the inclusion of galactooligosaccharide, galactosyllactose, synergy-oligosaccharide, bifidooligo and improvement material of gut microbiota contained in formula-milk. We conclude that 5-Bifidus factor contained in milk powder promotes the growth of Bifidobacterium genus in the intestines.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.169-174
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• 2019

In this study, I use surveillance data collection and data mining, clustered by clustering method, and use supervised learning to judge similarity. I aim to use feature extraction algorithms and supervised learning to analyze the suitability of the correlations of personality. After conducting the questionnaire survey, the researchers refine the collected data based on the questionnaire, classify the data sets through the clustering techniques of WEKA, an open source data mining tool, and judge similarity using supervised learning. I then use feature extraction algorithms and supervised learning to determine the suitability of the results for personality. As a result, it was found that the highest degree of similarity classification was obtained by EM classification and supervised learning by Naïve Bayes. The results of feature classification and supervised learning were found to be useful for judging fitness. I found that the accuracy of each Big-5 personality was changed according to the addition and deletion of the items, and analyzed the differences for each personality.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.97-102
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• 2019

There are many difficulties to define because there is no systematic classification and analysis method using accurate criteria or numerical values for creative people. In order to solve this problem, this study attempts to analyze how to distinguish creative people and what kind of personality they have when distinguishing creative people. In this study, I first survey the Big 5 personality trait, classify and analyze the data set using the data mining tool WEKA, and then analyze the data set related to the creativity The goal is to analyze the features using various machine learning techniques. I use seven feature selection algorithms, select feature groups classified by feature selection algorithms, apply them to machine learning algorithms to find out the accuracy, and derive the results.

• Journal of Korea Entertainment Industry Association
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• v.13 no.3
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• pp.317-332
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• 2019

The purpose of this study is to analyze the personality trait of game users' game behavior and to investigate the differences according to demographic variables. For research, questionnaire survey was conducted for game users of 10~ 40's, and the collected data was analyzed and processed using the statistics package program SPSS 20.0. The results of the study showed that the Big 5 personality traits had a significant impact on game use, and in the case of Conscientiousness, most of them were positive for use of Adaptive games and most of them had negative effects on Maladaptive game use. Even in personal characteristics, a variable showing a significant influence on game use was found, which showed meaningful effects in game platform, game frequency, and occupation. In subsequent research, it is necessary to identify the variables such as types of games or platforms that can reflect characteristics of games, and to understand what kind of roles play in the relationship between game user characteristics and game use behavior.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.989-998
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• 2013

We cannot analyze big data, which attracts recent attentions in industry and academy, by batch processing algorithms developed in data mining because big data, by definition, cannot be uploaded and processed in the memory of a single system. So an imminent issue is to develop various leaning algorithms so that they can be applied to big data. In this paper, we review various algorithms for support vector machines in the literature. Particularly, we introduce online type and parallel processing algorithms that are expected to be useful in big data classifications and compare the strengths, the weaknesses and the performances of those algorithms through simulations for linear classification.

• Fashion & Textile Research Journal
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• v.22 no.5
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• pp.639-649
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• 2020

This study investigates consumer opinions of clothing care and provides fundamental data to decision-making for oncoming development of clothing care system. Textom, a web-matrix program, was used to analyze big data collected from Naver and Daum with a keyword of "clothing care" from March 2019 to February 2020. A total of 22, 187 texts were shown from the big data collection. Collected big data were analyzed using text-mining, network, and CONCOR analysis. The results of this study were as follows. First, many keywords related to clothing care were shown from the result of frequency analysis such as style, Dryer, LG Electronics, Product, Customer, Clothing, and Styler. Consumers were well recognizing and having an interest in recent information related to the clothing care system. Second, various keywords such as product, function, brand, and performance, were linked to each other which were fundamentally related to the clothing care. The interest in products of the clothing care system were linked to product brands that were also naturally linked to consumer interest. Third, the keywords in the network showed similar attributes from the result of CONCOR analysis that were classified into 4 groups such as the characteristics of purchase, product, performance, and interest. Lastly, positive emotions including goodwill, interest, and joy on the clothing care system were strongly expressed from the result of the sentimental analysis.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• Industry Promotion Research
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• v.5 no.4
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• pp.91-100
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• 2020

In the U.S.A. from the late 19th to the early 20th century, the big business system emerged surpassing the British Empire economy. Such growth resulted from the realization of the "American-productive mode' being derived from the continuous immigrants inflow, renovative development of transportation, national markets formation and R&D of the science·technology. During 10 years after 1895, American economy was prevalent with the combination trends by the vertical or horizontal integration and these both mixed systems. As such big business was recognized, the American domestic citizens expressed the strong doubt to the revolutionary change and its public benefits and inaugurated the anti-big business campaign with deep concern that the American traditional symbol 'land of the wealth and opportunity' would be threatened. Although the governmental organizations controlling big business were established and the control laws were enforced, the American society accepted the new economic order. This situation resulted from the American economic prosperity, material affluence and managerialism of the big business.

• Information Systems Review
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• v.18 no.2
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• pp.39-57
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• 2016

Previous studies on the success factors of big data implementation have called for future research and further examination of the top management leadership's impact. This research proposes and empirically tests three hypotheses, including how top management leadership can directly affect big data investment, how it can mediate the causal relationship between big data investment and idea usefulness, and how it can mediate the relationship between idea usefulness and business utilization. Based on the data collected from 108 big data users in Korean companies, we determined that all three hypotheses are statistically significant. By shedding light on top management leadership and its characteristics, we can provide better suggestions on what needs to be done to ensure the success of big data.