• 공학교육연구
• /
• 제22권5호
• /
• 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.

• 한국융합학회논문지
• /
• 제9권2호
• /
• pp.251-257
• /
• 2018

빅데이터의 활용은 비즈니스 가치를 높이는데 필수요소가 됨에 따라 빅데이터 시장의 규모가 점점 더 커지고 있다. 이에 따라 빅데이터 시장을 선점하기 위해서는 경쟁력 있는 특허를 선점하는 것이 중요하다. 본 연구에서는 빅데이터 특허의 동향을 분석하기 위하여 영문 키워드 네트워크 기반 특허분석을 수행하였다. 분석 절차는 빅데이터 수집 및 전처리, 네트워크 구성, 네트워크 분석으로 구성되어 있다. 연구 결과는 다음과 같다. 빅데이터 특허 대다수는 예측 등을 위한 데이터 처리를 위한 특허이며, analysis, process, information, data, prediction, server, service, construction 키워드가 연결정도 중심성 및 매개 중심성이 높았다. 본 연구의 분석결과는 향후 빅데이터 특허 출원 시 참고할 수 있는 유용한 정보로 활용될 수 있다.

• International Journal of Internet, Broadcasting and Communication
• /
• 제15권2호
• /
• pp.112-117
• /
• 2023

This study aims to find out what tourists' interests and perceptions are like through online big data. Big data for a total of five years from 2018 to 2022 were collected using the Textom program. Sentiment analysis was performed with the collected data. Sentiment analysis expresses the necessity and emotions of city tours in online reviews written by tourists using city tours. The purpose of this study is to extract and analyze keywords representing satisfaction. The sentiment analysis program provided by the big data analysis platform "TEXTOM" was used to study positives and negatives based on sentiment analysis of tourists' online reviews. Sentiment analysis was conducted by collecting reviews related to the city tour. The degree of positive and negative emotions for the city tour was investigated and what emotional words were analyzed for each item. As a result of big data sentiment analysis to examine the emotions and sentiments of tourists about the city tour, 93.8% positive and 6.2% negative, indicating that more than half of the tourists are positively aware. This paper collects tourists' opinions based on the analyzed sentiment analysis, understands the quality characteristics of city tours based on the analysis using the collected data, and sentiment analysis provides important information to the city tour platform for each region.

• Communications for Statistical Applications and Methods
• /
• 제28권2호
• /
• pp.135-150
• /
• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• 공학교육연구
• /
• 제17권5호
• /
• pp.51-58
• /
• 2014

This study proposes an effective data management method for easing the burden on self-study report by analyzing operational data on accredited engineering programs. Four analysis criteria are developed: variability, difficulty level of collecting, urgency of analysis, timeliness. After the operational data are analyzed in terms of the analysis criteria, the data which should be managed in time are extracted according to the analysis results. This study proposes a data management method in which tasks of managing the timely-managed data are performed based on the regular academic schedule, so that the result of this study may be used as a working-level reference material.

• 한국품질경영학회:학술대회논문집
• /
• 한국품질경영학회 2010년도 춘계학술대회
• /
• pp.226-234
• /
• 2010

In this thesis, we present process that remove mistake of data before statistical analysis. If field data which is not simple examination about validity of data, we cannot believe analyzed statistics information. As statistical analysis information is produced based on data to be input in statistical analysis process, the data to be input should be free of error. In this paper, we study the application of statistical analysis road map that can enhance application on site by organizing basic theory and approaching on initial data exploratory phase, essential step before conducting statistical analysis. Therefore, access to statistical analysis can be enhanced and reliability on result of analysis can be secured by conducting correct statistical analysis.

• 품질경영학회지
• /
• 제51권4호
• /
• pp.551-571
• /
• 2023

Purpose: The purpose of this study is to propose useful suggestions by analyzing the causal effect relationship between the failure rate of quality and the process variables in the C5ISR domain of the defense industry. Methods: The collected data through the in house Systems were analyzed using Big data analysis. Data analysis between quality data and A/S history data was conducted using the CRISP-DM(Cross-Industry Standard Process for Data Mining) analysis process. Results: The results of this study are as follows: After evaluating the performance of candidate models for the influence of inspection data and A/S history data, logistic regression was selected as the final model because it performed relatively well compared to the decision tree with an accuracy of 82%/67% and an AUC of 0.66/0.57. Based on this model, we estimated the coefficients using 'R', a data analysis tool, and found that a specific variable(continuous maximum discharge current time) had a statistically significant effect on the A/S quality failure rate and it was analysed that 82% of the failure rate could be predicted. Conclusion: As the first case of applying big data analysis to quality issues in the defense industry, this study confirms that it is possible to improve the market failure rates of defense products by focusing on the measured values of the main causes of failures derived through the big data analysis process, and identifies improvements, such as the number of data samples and data collection limitations, to be addressed in subsequent studies for a more reliable analysis model.

• 한국항공운항학회지
• /
• 제23권4호
• /
• pp.6-11
• /
• 2015

In this study, we surveyed analysis of ADS-B ground trajectory data using non-aviation approval public data. For analysis used non-aviation public data and commercial ADS-B receiver. The study result is available using ADS-B ground trajectory data for airfield surveillance on limited range. Also, to confirmed of available using non-aviation public data for aviation research.

• International Journal of Advanced Culture Technology
• /
• 제11권3호
• /
• pp.332-337
• /
• 2023

The purpose of the study was to analyze consumers' perceptions of gamification. Based on the analyzed data, we would like to provide data by systematically organizing the concept, game elements, and mechanisms of gamification. Recently, gamification can be easily found around medical care, corporate marketing, and education. This study collected keywords from social media portal sites Naver, Daum, and Google from 2018 to 2023 using TEXTOM, a social media analysis tool. In this study, data were analyzed using text mining, semantic network analysis, and CONCOR analysis methods. Based on the collected data, we looked at the relevance and clusters related to gamification. The clusters were divided into a total of four clusters: 'Awareness of Gamification', 'Gamification Program', 'Future Technology of Gamification', and 'Use of Gamification'. Through social media analysis, we want to investigate and identify consumers' perceptions of gamification use, and check market and consumer perceptions to make up for the shortcomings. Through this, we intend to develop a plan to utilize gamification.

• 공학교육연구
• /
• 제26권4호
• /
• pp.22-35
• /
• 2023

As information and communication technology has developed remarkably, it has become possible to analyze various types of large-volume data generated at a speed close to real time, and based on this, reliable value creation has become possible. Such big data analysis is becoming an important means of supporting decision-making based on scientific figures. The purpose of this study is to develop a big data analysis tool that can analyze large amounts of data generated through engineering education. The tasks of this study are as follows. First, a database is designed to store the information of entries in the National Creative Capstone Design Contest. Second, the pre-processing process is checked for analysis with big data analysis tools. Finally, analyze the data using the developed big data analysis tool. In this study, 1,784 works submitted to the National Creative Comprehensive Design Contest from 2014 to 2019 were analyzed. As a result of selecting the top 10 words through topic analysis, 'robot' ranked first from 2014 to 2019, and energy, drones, ultrasound, solar energy, and IoT appeared with high frequency. This result seems to reflect the current core topics and technology trends of the 4th Industrial Revolution. In addition, it seems that due to the nature of the Capstone Design Contest, students majoring in electrical/electronic, computer/information and communication engineering, mechanical engineering, and chemical/new materials engineering who can submit complete products for problem solving were selected. The significance of this study is that the results of this study can be used in the field of engineering education as basic data for the development of educational contents and teaching methods that reflect industry and technology trends. Furthermore, it is expected that the results of big data analysis related to engineering education can be used as a means of preparing preemptive countermeasures in establishing education policies that reflect social changes.