• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2704-2719
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• 2022

Lately, neo-Chinese style furniture has been frequently noticed by product design professionals for the big part it played in promoting traditional Chinese culture. This article is an attempt to use big data semantic analysis method to provide effective design research method for neo-Chinese furniture design. By using big data mining program TEXTOM for big data collection and analysis, the data obtained from typical websites in a set time period will be sorted and analyzed. On the basis of "neo-Chinese furniture" samples, key data will be compared, classification analysis of overall data, and horizontal analysis of typical data will be performed by the methods of word frequency analysis, connection centrality analysis, and TF-IDF analysis. And we tried to summarize according to the related views and theories of the design. The research results show that the results of data analysis are close to the relevant definitions of design. The core high-frequency vocabulary obtained under data analysis, such as popular, furniture, modern, etc., can provide a reasonable and effective focus of attention for the designs. The result obtained through the systematic sorting and summary of the data can be a reliable guidance in the direction of our design. This research attempted to introduce related big data mining semantic analysis methods into the product design industry, to supply scientific and objective data and channels for studies on design, and to provide a case on the practical application of big data analysis in the industry.

• 한국멀티미디어학회논문지
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• 제18권4호
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• pp.524-532
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• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

• 공학교육연구
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• 제22권5호
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• pp.37-48
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• 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.

• 한국측량학회지
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• 제34권6호
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• pp.569-578
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• 2016

With the recent advancement of surveying and technology, the spatial data acquisition rates and precision have been improved continually. As the updates of spatial data are rapid, and the size of data increases in line with the advancing technology, the LOD (Level of Detail) algorithm has been adopted to process data expressions in real time in a streaming format with spatial data divided precisely into separate steps. The existing GRID analysis utilizes the single DEM, as it is, in examining and analyzing all data outside the analysis area as well, which results in extending the analysis time in proportion to the quantity of data. Hence, this study suggests a method to reduce analysis time and data throughput by acquiring and analyzing DEM data necessary for GRID analysis in real time based on the area of analysis and the level of precision, specifically for streaming DEM data, which is utilized mostly for 3D geographic information service.

• 한국컴퓨터정보학회논문지
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• 제21권8호
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• pp.77-84
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• 2016

In general statistical analysis, we need to make a normal assumption. If this assumption is not satisfied, we cannot expect a good result of statistical data analysis. Most of statistical methods processing the outlier and noise also need to the assumption. But the assumption is not satisfied in big data because of its large volume and heterogeneity. So we propose a methodology based on box-plot and data smoothing for controling outlier and noise in big data analysis. The proposed methodology is not dependent upon the normal assumption. In addition, we select patent documents as target domain of big data because patent big data analysis is a important issue in management of technology. We analyze patent documents using big data learning methods for technology analysis. The collected patent data from patent databases on the world are preprocessed and analyzed by text mining and statistics. But the most researches about patent big data analysis did not consider the outlier and noise problem. This problem decreases the accuracy of prediction and increases the variance of parameter estimation. In this paper, we check the existence of the outlier and noise in patent big data. To know whether the outlier is or not in the patent big data, we use box-plot and smoothing visualization. We use the patent documents related to three dimensional printing technology to illustrate how the proposed methodology can be used for finding the existence of noise in the searched patent big data.

• 한국융합학회논문지
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• 제9권2호
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• pp.251-257
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• 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.

• 풍력에너지저널
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• 제4권1호
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• pp.20-29
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• 2013

Load measurement, which is performed based on IEC 61400-13, consists of three stages: the stage of collecting huge amounts of load measurement data through a measurement campaign lasting for several months; the stage of processing the measured data, including data validation and classification; and the stage of analyzing the processed data through time series analysis, load statistics analysis, frequency analysis, load spectrum analysis, and equivalent load analysis. In this research, we pursued the development of an analysis software in MATLAB to save labor and to secure exact and consistent performance evaluation data in processing and analyzing load measurement data. The completed analysis software also includes the functions of processing and analyzing power performance measurement data in accordance with IEC 61400-12. The analysis software was effectively applied to process and analyse the load measurement data from a demonstration research for a 750 kW direct-drive wind turbine generator system (KBP-750D), performed at the Daegwanryeong Wind Turbine Demonstration Complex. This paper describes the details of the analysis software and its processing and analysis stages for load measurement data and presents the analysis results.

• 디지털콘텐츠학회 논문지
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• 제19권3호
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• pp.529-535
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• 2018

데이터는 폭발적으로 증가하고 있으나 아직도 많은 기업이 데이터 분석을 현황 설명(descriptive analysis)이나 진단 분석(diagnostic analysis)에만 활용하고 예측분석(predictive analysis)이나 기업의 기술전략 분석 등에는 적절하게 활용하고 있지 못하다. 본 연구는 오픈 되어 있는 특허의 IPC 코드, 발명자, 출원일 등의 정형데이터와 청구항 등의 비정형 데이터를 네트워크분석, TF-IDF 등의 빅데이터 분석기법을 활용하여 경쟁기업의 확보 기술과 핵심 기술의 분포, 해외 진출 전략을 파악하기 위한 분석 프로세스를 제시하고 이를 데이터 분석을 통하여 증명하고자 한다.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.53-62
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• 2006

DNA microarray data analysis is a new technology to investigate the expression levels of thousands of genes simultaneously. Since DNA microarray data structures are various and complicative, the data are generally stored in databases for approaching to and controlling the data effectively. But we have some difficulties to analyze and control the data when the data are stored in the several database management systems or that the data are stored to the file format. The existing analysis tools for DNA microarray data have many difficult problems by complicated instructions, and dependency on data types and operating system. In this paper, we design and implement network-based analysis tool for obtaining to useful information from DNA microarray data. When we use this tool, we can analyze effectively DNA microarray data without special knowledge and education for data types and analytical methods.

• Journal of the Korean Statistical Society
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• 제30권2호
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• pp.219-230
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• 2001

How do you carry out data analysis\ulcorner There are few texts and little theory. One approach could be to use a pattern language, an idea which has been successful in field as diverse as town planning and software engineering. Patterns for data analysis are defined and discussed, illustrated with examples.