• Title/Summary/Keyword: 보편적 빅데이터

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Study on the Direction of Universal Big Data and Big Data Education-Based on the Survey of Big Data Experts (보편적 빅데이터와 빅데이터 교육의 방향성 연구 - 빅데이터 전문가의 인식 조사를 기반으로)

  • Park, Youn-Soo;Lee, Su-Jin
    • Journal of The Korean Association of Information Education
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    • v.24 no.2
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    • pp.201-214
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    • 2020
  • Big data is gradually expanding in diverse fields, with changing the data-related legislation. Moreover it would be interest in big data education. However, it requires a high level of knowledge and skills in order to utilize Big Data and it takes a long time for education spends a lot of money for training. We study that in order to define Universal Big Data used to the industrial field in a wide range. As a result, we make the paradigm for Big Data education for college students. We survey to the professional the Big Data definition and the Big Data perception. According to the survey, the Big Data related-professional recognize that is a wider definition than Computer Science Big Data is. Also they recognize that the Big Data Processing dose not be required Big Data Processing Frameworks or High Performance Computers. This means that in order to educate Big Data, it is necessary to focus on the analysis methods and application methods of Universal Big Data rather than computer science (Engineering) knowledge and skills. Based on the our research, we propose the Universal Big Data education on the new paradigm.

Research on the Analysis System based on the Big Data for Matlab (Matlab을 활용한 빅데이터 기반 분석 시스템 연구)

  • Joo, Moon-il;Kim, Hee-cheol
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2016.10a
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    • pp.96-98
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    • 2016
  • Recently, big data technology develop due to the rapid data generation. Thus big data analysis tools for analyzing big data has been developed. Typical big data tools are the R program, Hive, Tajo and more. But data analysis based on Matlab is still common used. And it is still used in big data analysis. In this paper, it research into big data analysis system based on the Matlab for analyzing vital signals.

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An Analysis of High School Korean Language Instruction Regarding Universal Design for Learning: Social Big Data Analysis and Survey Analysis (보편적 학습설계 측면에서의 고등학교 국어과 교수 실태: 소셜 빅데이터 및 설문조사 분석)

  • Shin, Mikyung;Lee, Okin
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.1
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    • pp.326-337
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    • 2020
  • This study examined the public interest in high school Korean language instruction and the universal design for learning (UDL) using the social big data analysis method. The observations from 10,339 search results led to the conclusion that public interest in UDL was significantly lower than that of high school Korean language instruction. The results of the Big Data Association analysis showed that 17.22% of the terms were found to be related to "curriculum." In addition, a survey was conducted on a total of 330 high school students to examine how their teachers apply UDL in the classroom. High school students perceived computers as the most frequently used technology tool in daily classes (38.79%). Teacher-led lectures (52.12%) were the most frequently observed method of instruction. Compared to the second-year and third-year students, the first-year students appreciated the usage of technology tools and various instruction mediums more frequently (ps<.05). Students were relatively more positive in their response to the query on the provision of multiple means of representation. Consequently, the lesson contents became easier to understand for students with the availability of various study methods and materials. The first-year students were generally more positive towards teachers' incorporation of UDL.

A Study on Risks of Big Data (빅데이터의 위험 요소에 대한 고찰)

  • Yoonsoo Cheon;Jaekyung Park
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2023.07a
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    • pp.631-633
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    • 2023
  • 본 논문에서는 빅데이터의 활용이 확산되는 현대 사회에서 빅데이터의 수집, 관리, 이용 등에서 나타날 수 있는 문제를 확인하고 그 문제에 대한 기존의 대응 방법과 보완점을 시사한다. 빅데이터의 위험성은 개인 정보유출, 디지털 디바이드, 편향성과 신뢰성, 의존성과 통제 가능성 등이 있다. 해당 문제는 빅데이터의 보편화가 가중될수록 큰 규모의 사회적 문제로 대두될 가능성이 높다. 이를 보완하기 위한 대응 방법을 크게 기술적 대응, 법적 대응, 사회적 대응으로 나누어 알아보고 각 부분의 취약점을 분석하여 개선의 방향을 제시한다.

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A Case Study on Big Data Analysis of Performing Arts Consumer for Audience Development (관객개발을 위한 공연예술 소비자 빅데이터 분석 사례 고찰)

  • Kim, Sun-Young;Yi, Eui-Shin
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.12
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    • pp.286-299
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    • 2017
  • The Korean performing arts has been facing stagnation due to oversupply, lack of effective distribution system, and insufficient business models. In order to overcome these difficulties, it is necessary to improve the efficiency and accuracy of marketing by using more objective market data, and to secure audience development and loyalty. This study considers the viewpoint that 'Big Data' could provide more general and accurate statistics and could ultimately promote tailoring services for performances. We examine the first case of Big Data analysis conducted by a credit card company as well as Big Data's characteristics, analytical techniques, and the theoretical background of performing arts consumer analysis. The purpose of this study is to identify the meaning and limitations of the analysis case on performing arts by Big Data and to overcome these limitations. As a result of the case study, incompleteness of credit card data for performance buyers, limits of verification of existing theory, low utilization, consumer propensity and limit of analysis of purchase driver were derived. In addition, as a solution to overcome these problems, it is possible to identify genre and performances, and to collect qualitative information, such as prospectors information, that can identify trends and purchase factors.combination with surveys, and purchase motives through mashups with social data. This research is ultimately the starting point of how the study of performing arts consumers should be done in the Big Data era and what changes should be sought. Based on our research results, we expect more concrete qualitative analysis cases for the development of audiences, and continue developing solutions for Big Data analysis and processing that accurately represent the performing arts market.

Analysis of Sales Volume by Products According to Temperature Change Using Big Data Analysis (빅데이터 분석을 통한 기온 변화에 따른 상품의 판매량 분석)

  • Hong, Jun-Ki
    • The Journal of Bigdata
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    • v.4 no.2
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    • pp.85-91
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    • 2019
  • Since online shopping has become common, people can easily buy fashion goods anytime, anywhere. Therefore, consumers quickly respond to various environmental variables such as weather and sales prices. Thus, utilizing big data for efficient inventory management has become very important in the fashion industry. In this paper, the changes in sales volume of fashion goods due to changes in temperature is analyzed via the proposed big data analysis algorithm by utilizing actual big data from Korean fashion company 'B'. According to the analytic results, the proposed big data analysis algorithm found both expected and unexpected changes in sales volume depending on the characteristics of the fashion goods.

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A Study on Heterogenous Big Data Processing Platforms for Smart Factory (스마트 공장을 위한 이기종 빅데이터 처리 플랫폼에 대한 연구)

  • Song, Je-O;Cho, Jung-Hyun;Kwon, Jin-Gwan;Lee, Sang-Moon
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2019.07a
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    • pp.335-336
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    • 2019
  • 5G를 비롯한 무선 네트워크의 발달과 인터넷의 보급이 보편화되어 가고 있다. 또한, 스마트폰 등의 모바일 기기 등이 일상화됨에 따라 방대하고 다양한 유형의 데이터들이 발생되고 있다. 이와 같은 범람하기 시작한 정보와 데이터들을 연결하여 새로운 가치를 창출하는 초지능 연결의 4차 산업혁명 시대가 도래하였다. 이러한 4차 산업혁명은 ICBM(IoT, Cloud, Big data, Mobile) 기술이 발달함에 따라 가능했으며. 그중 빅데이터는 초지능 연결의 근간이 되고 있다. 하지만, 빅데이터에서의 데이터는 다양한 목적에 의해 다양한 유형의 데이터를 모두 포함하고 있음에도 데이터 포맷 및 데이터 셋 등의 불일치에 의해 즉각적인 연결은 불가능하다. 본 논문에서는 스마트 공장을 중심으로 서로 다른 형태의 이기종 데이터를 통합하여 처리할 수 있는 빅데이터 처리 플랫폼을 제안한다.

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Boxoffice Prediction Using Big Data (빅데이터를 이용한 박스오피스 예측)

  • Lee, Hyeong-Seok;Jeong, Gun-Mo;Lee, Min-Soo;Cheon, Jun-Hyeon;Kang, Yunjeong
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2017.07a
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    • pp.358-359
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    • 2017
  • 실제 영화관에서는 매출을 최대화하기 위해 저마다의 상영관 별 다른 영화 배치 전략을 가지고 있다. 이 영화 배치 전략으로 인해 영화관의 매출이 좌지우지 된다. 여기서 가장 보편적인 기준은 박스오피스이다. 하지만 박스오피스는 과거 영화 상영의 매출액을 모아둔 것으로 개봉되지 않은 영화에 대한 정보는 가지고 있지 않다. 이 개봉되지 않은 영화에 대한 기준, 즉 박스오피스를 얼마나 정확하게 예측 할 수 있는지가 각 영화관의 경쟁력을 결정한다. 본 논문은 개봉 예정인 영화들을 분석하고 이를 통해 박스오피스를 예측는 방법을 제시하고, 실제 박스오피스와 비교, 분석하는 내용을 다룬다.

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A Study on the Public Interest of Collected Information (수집된 정보의 공익성에 관한 고찰)

  • Park, Kook-Heum
    • Informatization Policy
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    • v.26 no.1
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    • pp.25-45
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    • 2019
  • With the advent of the data economy, interest in using big data has increased, but conflicts with protecting personal information have been also steadily raised. In this regard, major countries are accelerating use of big data by exempting de-identified, pseudonymous personal information from protection. However, these policies have been made without the understanding that the economic value of personal information has been actually changing slowly. This paper presents the concept of 'collected information' and defines it as having public interest and therefore, not the exclusive property of the collector of such information. The paper shows the collected information has public interest in terms of personal information protection, connectivity, and universal service and public goods. It also specifies that the 'data governance' cannot be applied to the current data utilization framework that depends upon the holder's consent; rather, it raises the need to improve the practices of information provision consent or provide the beneficiary right of information use to the information holder in order to ensure the proper 'data governance' that will turn market failure into success.

Implement of MapReduce-based Big Data Processing Scheme for Reducing Big Data Processing Delay Time and Store Data (빅데이터 처리시간 감소와 저장 효율성이 향상을 위한 맵리듀스 기반 빅데이터 처리 기법 구현)

  • Lee, Hyeopgeon;Kim, Young-Woon;Kim, Ki-Young
    • Journal of the Korea Convergence Society
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    • v.9 no.10
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    • pp.13-19
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    • 2018
  • MapReduce, the Hadoop's essential core technology, is most commonly used to process big data based on the Hadoop distributed file system. However, the existing MapReduce-based big data processing techniques have a feature of dividing and storing files in blocks predefined in the Hadoop distributed file system, thus wasting huge infrastructure resources. Therefore, in this paper, we propose an efficient MapReduce-based big data processing scheme. The proposed method enhances the storage efficiency of a big data infrastructure environment by converting and compressing the data to be processed into a data format in advance suitable for processing by MapReduce. In addition, the proposed method solves the problem of the data processing time delay arising from when implementing with focus on the storage efficiency.