• Journal of KIISE
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• v.43 no.6
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• pp.613-620
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• 2016

This paper presents a MOHA(Many-Task Computing on Hadoop) framework which aims to effectively apply the Many-Task Computing(MTC) technologies originally developed for high-performance processing of many tasks, to the existing Big Data processing platform Hadoop. We present basic concepts, motivation, preliminary results of PoC based on distributed message queue, and future research directions of MOHA. MTC applications may have relatively low I/O requirements per task. However, a very large number of tasks should be efficiently processed with potentially heavy inter-communications based on files. Therefore, MTC applications can show another pattern of data-intensive workloads compared to existing Hadoop applications, typically based on relatively large data block sizes. Through an effective convergence of MTC and Big Data technologies, we can introduce a new MOHA framework which can support the large-scale scientific applications along with the Hadoop ecosystem, which is evolving into a multi-application platform.

• Korean Journal of Construction Engineering and Management
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• v.18 no.6
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• pp.13-25
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• 2017

Apartments are a major type of residence and their number has continuously increased. Apartments have multiple meanings in that for public they are not only for residence purpose but for investment, a major commodity for construction firms and a critical policy measure of public well-fare for the government. Therefore, it is critical to understand and analyze trends in apartments demand for pro-active actions. The objective of the study is to analyze and identify key trends in apartments demand based on big data drawn from articles of major daily newspapers. The study identifies 17 major trends from seven themes including development, trade, sale in lots, location requirements, policy, residential environment, and investment and profit. The research methods in the study can be usefully applied to further studies for various issues in relation to the construction industry.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.1166-1174
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• 2015

There were the attempts to prevent the spread of harmful materials of the agri-food through the record tracking of the products with the bar code, the partial information tracking of the agri-food storage and the delivery vehicle, or the control of the temperature by intuition. However, there were many problems in the attempts because of the insufficient information, the information distortion and the independent information network of each distribution company. As a result, it is difficult to prevent the spread over the life-cycle of the agri-food using the attempts. To solve the problems, we propose the mechanism mainly to do context awareness, predict, and track the harmful materials of agri-food using big data processing.

• Journal of Digital Convergence
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• v.16 no.6
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• pp.363-370
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• 2018

The purpose of this study was to investigate the relationship between big five factors and burnout in non-commissioned officers. To investigate this, 182 non-commissioned officers were surveyed and the data were analyzed. At first, the degree of burnout of the non-commissioned officer was examined. It was found that exhaustion was the highest and diminished personal accomplishment was the lowest among the three sub-factors of burnout. In the 20s and 30s, burnout(total) and cynicism were more severe than in the 40s and above. Then, the multiple regression analysis and the multivariate multiple regression analysis were conducted. As a result, extroversion and agreeableness were related to burnout(total) and extroversion was related to the three dimensions of burnout, The results are likely to help establish policies to prevent non-commissioned officers' burnout. Finally, the limitations of this study were discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.175-181
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• 2014

With rapid growth of the wireless mobile computing network technologies, various mobile ad hoc network applications converged with other related technologies are rapidly disseminated nowadays. Vehicular Ad Hoc Networks are self-organizing mobile ad hoc networks that typically have moving vehicle nodes with high speeds and maintaining its topology very short with unstable communication links. Therefore, VANETs are very vulnerable for the malicious noise of sensors and anomalies of the nodes in the network system. In this paper, we propose an anomaly detection method by using big data techniques that efficiently identify malicious behaviors or noises of sensors and anomalies of vehicle node activities in these VANETs, and the performance of the proposed scheme is evaluated by a simulation study in terms of anomaly detection rate and false alarm rate for the threshold ${\epsilon}$.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.275-282
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• 2020

Social interest on high-quality specialty coffee is increased due to customers' growing experience upon coffee and recent change of coffee culture, which is taking one step further from putting emphasis on not just price and quality but also psychological satisfaction. As a culture of drinking coffee and giving much value on its taste and flavor, a number of customers increasingly demand coffee which is probable to suit one's taste. Likewise, the number of specialty coffee shops is increasing with growing qualities of their coffee. Therefore, the purpose of this study is to analyze the main attributes of specialty coffee and to build a marketing system for specialty coffee shops. The text mining on domestic web portal sites by online big-data analysis is used to extract components of properties of specialty coffee and analyze the degree of how the elements affect the properties. According to the result of the study, words related to coffee taste, coffee beans and baristas were found to play a central role in the properties of specialty coffee.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1007-1023
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• 2016

This paper explores the conditions and potential of newly designed and tried methodology of big data analysis that apply to Korean history subject matter. In order to advance them, we need to pay more attention to quantitative analysis methodologies over pre-existing qualitative analysis. To obtain our new challenge, I propose 'digital history' methods along with associated disciplines such as linguistics and computer science, data science and statistics, and visualization techniques. As one example, I apply interdisciplinary convergence approaches to the principle and mechanism of elite reproduction during the Korean medieval age. I propose how to compensate for a lack of historical material by applying a semi-supervised learning method, how to create a database that utilizes text-mining techniques, how to analyze quantitative data with statistical methods, and how to indicate analytical outcomes with intuitive visualization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.829-834
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• 2018

As the IT technology is progressing, the big data becomes more important and is exploited on the various industry. The R is the language and the environment analyzing the big data. The university which is the highest level of the academic organization keeps opening and maintaining the departments anticipating the needs of the progressing trends. As analyzing the names of the departments opened at the universities, it is possible to find out the requirements and the needs of the recent trends. In this paper, we analyze the names of the departments presented at the 4 year universities using the R. To do this, we collect the names of the departments and measure the frequency of the names in order to know the department of major frequently presented at the universities.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1259-1269
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• 2015

Recently, big data is positioning as a keyword in the academic circles. And usefulness of big data is carried into government, a local public body and enterprise as well as academic circles. Also they are endeavoring to obtain useful information in big data. This research mainly deals with analyses of box office success or failure of films using text mining. For data, it used a portal site 'D' and film review data, grade point average and the number of screens gained from the Korean Film Commission. The purpose of this paper is to propose a model to predict whether a film is success or not using these data. As a result of analysis, the correct classification rate by the prediction model method proposed in this paper is obtained 95.74%.

• Journal of the Korean Home Economics Association
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• v.41 no.6
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• pp.1-12
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• 2003

The purpose of this study were to analyze the anthropometric data of feet of Korean women with aging, to categorize the women's foot shapes, and to compare the shoe size according to the foot shapes in order to provide the basic information for more comfortable shoes. Subjects of this study were 181 women over age 20. They were measured with the direct measurement method and the indirect measurement method. 26 items were measured from the right foot and 6 items were taken on foot outline. Factor analysis, cluster analysis, analysis of variance, post-hoc test, and cross tabs were peformed for statistical analysis of the data by SPSS program. There were significant differences in height items, breadth items, girth items, and angle items by subjects' age. The older subjects' feet were wide and thick with big deformity on toes. The arch height of the older ones was low. This implicates that the degree of deformity on toes, the foot ratio, the foot girth, the foot breath and the arch height as well as the foot length are needed to be considered in developing comfortable shoes. Nine foot construction factors were extracted by the factor analysis of anthropometric measurements; foot size factor, heel and instep factor, malleolus lateralis factor, malleolus medialis factor, foot shape factor, shape of toes factor, heel height factor, big toe height factor, and internal factor. On the basis of the cluster analysis, three different foot shapes were categorized. Type 1 was large and wide foot with little deformity on little toe. Type 2 was medium foot with deformation of big toe, and with the lowest arch height. Type 3 was small and narrow foot with the highest arch height. Distribution of shoe size according to the foot shape was analyzed. The ball of foot breath was of wide distribution than the ball of foot girth. This implicates that girth items and breath items of the foot should be enclosed for the same foot length in the shoe sizing system.