• Journal of Information Processing Systems
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• 제12권2호
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• pp.234-248
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• 2016

Due to the proliferation of data being exchanged and the increase of dependency on this data for critical decision-making, it has become imperative to ensure the trustworthiness of the data at the receiving end in order to obtain reliable results. Data provenance, the derivation history of data, is a useful tool for evaluating the trustworthiness of data. Various frameworks have been proposed to evaluate the trustworthiness of data based on data provenance. In this paper, we briefly review a history of these frameworks for evaluating the trustworthiness of data and present an overview of some prominent state-of-the-art evaluation frameworks. Moreover, we provide a comparative analysis of two key frameworks by evaluating various aspects in an executional environment. Our analysis points to various open research issues and provides an understanding of the functionalities of the frameworks that are used to evaluate the trustworthiness of data.

• 대한가정학회지
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• 제43권11호
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• pp.185-196
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• 2005

The purpose of this research was to determine the relation between clothing evaluative criteria and segmented groups based on the perception of somatotype. The data for this research were collected from questionnaires of 192 females in Busan. Data were analyzed by frequency, factor analysis, cluster analysis, discriminant analysis, and regression analysis. Cluster analysis was used to identify groups of respondents based on the perception of somatotype difference factors. Based on the findings, three distinct groups were clustered: thin, moderate, fat. There were significant differences among the three groups in terms of clothing evaluative criteria. The result of regression analysis revealed that the perception of somatotype is a major determinant to influence the clothing evaluative criteria. The thin group preferred practical clothes while the fat group liked symbol clothes.

• Journal of the Korean Data and Information Science Society
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• 제16권2호
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• pp.217-226
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• 2005

Cluster analysis has been widely used in many applications, such as data analysis, pattern recognition, image processing, etc. But clustering requires many hours to get clusters that we want, because it is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new clustering method, 'Clustering algorithm using a center of gravity for grid-based sample'. It reduces running time by using grid-based sample and keeps accuracy by using representative point, a center of gravity.

• 보건의료산업학회지
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• 제13권3호
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• pp.127-136
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• 2019

Objectives: This study examined the determinants of quality of life, depending on the presence or absence of asthma in adults, based on secondary wave data. Methods: Among the 21,724 people participating in the 6th Korea National Health and Nutrition Examination Survey as it was conducted from the first to third period, 495 participants who were aged 19 or older and responded to the question of the presence or absence of asthma were included in the final analysis. Demographic characteristics were examined using the SPSS/WIN 23.0 software tool for analysis of complex sample survey data. Health-related characteristics were presented using descriptive and multivariate analysis of data. Rao_Scott ${\chi}^2$ was used for the analysis of differences in quality of life, and multiple regression analysis of complex sample survey data was used to analyze factors affecting quality of life. Results: The variable factors negatively influencing quality of life were aging, cognition of their ill health, and limited activities. Conclusions: Based on the analysis, the study suggests that practical and ongoing nursing intervention proposals to improve the quality of life of asthmatic patients should be implemented not only for physical limitations and aging but also for psychological factors that reflect subjective health statuses.

• International Journal of Internet, Broadcasting and Communication
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• 제14권4호
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• pp.228-239
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• 2022

Many countries around the world using ICT technologies have various technologies to keep pace with the 4th industrial revolution, and various algorithms and systems have been developed accordingly. Among them, many industries and researchers are investing in unmanned automation systems based on AI. At the time when new technology development and algorithms are developed, decision-making by big data analysis applied to AI systems must be equipped with more sophistication. We apply, Pearson's correlation analysis is applied to six independent variables to find out the job satisfaction that office workers feel according to their job characteristics. First, a correlation coefficient is obtained to find out the degree of correlation for each variable. Second, the presence or absence of correlation for each data is verified through hypothesis testing. Third, after visualization processing using the size of the correlation coefficient, the degree of correlation between data is investigated. Fourth, the degree of correlation between variables will be verified based on the correlation coefficient obtained through the experiment and the results of the hypothesis test

• 디지털콘텐츠학회 논문지
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• 제16권4호
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• pp.545-553
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• 2015

모든 데이터는 그 자체로 가치를 가지고 있지만, 실세계에서 수집되는 데이터들은 무작위적이며 비구조화되어 있다. 따라서 이러한 데이터를 효율적으로 활용하기 위해서 데이터에서 유용한 정보를 추출하기 위한 데이터 변환과 분석 알고리즘들을 사용하게 된다. 이러한 목적으로 사용되는 것이 데이터 마이닝이다. 오늘날에는 데이터를 분석하기 위한 다양한 데이터 마이닝 기법뿐만 아니라, 대용량 데이터를 효율적으로 처리하기 위한 연산 요건과 빠른 분석 시간을 필요로 하고 있다. 대용량 데이터를 저장하기 위하여 하둡이 많이 사용되며, 이 하둡의 데이터를 분석하기 위하여 맵리듀스 프레임워크를 사용한다. 본 논문에서는 단일 머신에서 동작하는 알고리즘을 맵리듀스 프레임워크로 개발할 때 적용의 효율성을 높이기 위한 토너먼트 기반 적용 방안을 제안하였다. 본 방법은 다양한 알고리즘에 적용할 수 있으며, 널리 사용되는 데이터 마이닝 알고리즘인 k-means, k-근접 이웃 분류에 적용하여 그 유용성을 보였다.

• 한국전자통신학회논문지
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• 제19권4호
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• pp.763-770
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• 2024

현재 해수를 이용하는 육상 양식장에서는 실시간 수질 모니터링 및 시설 자동화 시스템, 용존산소 자동 공급장치 등 다양한 장비를 도입하여 사용하고 있다. 또한 양식장의 다양한 장비에서 수집되는 데이터는 수질 환경, 시설 운영, 작업장 영상정보 등 정형, 비정형 형태의 빅데이터를 생산한다. 양식장 운영 환경에서 생산되는 빅데이터는 운영 및 생산 효율 개선을 목표로 다양한 방법을 개발하고 적용을 시도하고 있다. 본 연구에서는 육상 양식장에서 생산되는 빅데이터를 효과적으로 분석하고 시각화하기 위한 시스템을 개발하는 것을 목표로, 양식장 빅데이터 분석 시스템에서 활용이 가능한 데이터 시각화 프로세스를 제시하고 빅데이터 시각화 도구를 개발하고 결과를 비교한다. 그리고 시계열 특성을 가지는 빅데이터의 비교 및 탐색이 직관적인 시각화 모델을 제시한다.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data (a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology fur sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• 보건행정학회지
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• 제10권4호
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• pp.75-98
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• 2000

This study applied Data Envelopment Analysis(DEA) and Ratio Analysis and Regression Analysis to a set of Korean Public Corporation Medical Centers to evaluate their relative human resource efficiencies. The output measure used in this study was based on health insurance system which was used in both in-patient departments and out-patient departments. Inputs included working time of the doctors, nurses, technicians, and managerial department staff. Based on the data provided on the inputs and outputs, the analysis showed 23 of the 34 hospitals to be relatively inefficient. Each hospital with an efficiency rating of less than 1 was considered relatively inefficient. In addition, managerial strategies based on dual variables were constructed to indicate the manner In which inefficient hospitals may be made efficient. A subsequent analysis of t-test revealed that the bed occupancy rate, medical revenue per 100beds, value added revenue per staff, medical revenue per staff were statistically significant. The results of this study suggest the DEA is a promising tool for evaluating relative human resource efficiency in hospitals which have multiple inputs and outputs and where the efficient production function is not specifiable with any precision. But it is considered that efficiency evaluations may be most effective]y accomplished by Incorporating a combination of methodologies such as ratio analysis and regression analysis.

• 정보교육학회논문지
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• 제25권6호
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• pp.917-925
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• 2021

본 연구의 목적은 모듈형 데이터 분석 도구를 활용하여 구성주의 기반의 교수학습방법을 구체화하는데 있다. 인공지능교육을 위한 내용기준에서 제시하는 인공지능이 적용된 도구로서 모듈형 데이터 분석도구가 갖는 가치와 의미를 살펴보고 컴퓨팅사고력을 기반으로 문제해결력을 기르는 단계와 과정을 살펴보고자 하였다. 모듈형 데이터분석 도구는 구성주의적 관점에서 동화와 조절을 통해 평형화를 이루는 과정에서 스키마를 형성하는 인지적 사고절차를 시각적으로 표현함으로서 인공지능에서 데이터의 구조를 형상화하는 특징을 갖고 있는 도구라는 장점을 갖는다. AI교육은 문제해결의 절차를 알고리즘으로 구현된 블랙박스로서의 표상화된 스키마를 적용한다는 점에서 데이터 분석의 모듈을 구조화하고 추상적 지식의 구조를 구체화하는 특징을 갖는다고 할 수 있다. 따라서 개념적 스키마와 내재적 스키마를 연결하는 도구로서의 장점을 갖는다는 점에서 모듈형 데이터 분석 도구의 활용가치를 살펴볼 수 있다.