• Title/Summary/Keyword: BIG4

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Big Data Analysis and Processing for Remote Control of PV Facilities (태양광발전설비 원격 관제를 위한 빅데이터 분석 및 처리)

  • Kwon, Jun-A;Kim, Young-Geun;Lee, Jong-Chan;Kim, Won-Jung
    • The Journal of the Korea institute of electronic communication sciences
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    • v.13 no.4
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    • pp.837-844
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    • 2018
  • In order to increase the generation of renewable energy, it is necessary to increase or decrease the generation amount of existing generators. The generators that respond rapidly to increase / decrease the generation amount generally have high generation cost. Therefore, Cost effectiveness is affected. In this paper, we propose a PV remote control system with big data to minimize the uncertainty of solar power generation prediction.

Analysis of Regional Transit Convenience in Seoul Public Transportation Networks Using Smart Card Big Data (스마트카드 빅데이터를 이용한 서울시 지역별 대중교통 이동 편의성 분석)

  • Moon, Hyunkoo;Oh, Kyuhyup;Kim, SangKuk;Jung, Jae-Yoon
    • Journal of Korean Institute of Industrial Engineers
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    • v.42 no.4
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    • pp.296-303
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    • 2016
  • In public transportation, smart cards have been introduced for the purpose of convenient payment systems. The smart card transaction data can be utilized not only for the exact and convenient payment but also for civil planning based on travel tracking of citizens. This paper focuses on the analysis of the transportation convenience using the smart card big data. To this end, a new index is developed to measure the transit convenience of each region by considering how passengers actually experience the transportation network in their travels. The movement data such as movement distance, time and amount between regions are utilized to access the public transportation convenience of each region. A smart card data of five working days in March is used to evaluate the transit convenience of each region in Seoul city. The contribution of this study is that a new transit convenience measure was developed based on the reality data. It is expected that this measure can be used as a means of quantitative analysis in civil planning such as a traffic policy or local policy.

A Study on Employment Strategy Based on Employment Information Filtering (취업정보 필터링 기반 취업전략에 관한 연구)

  • Yoon, Sunhee
    • The Journal of the Convergence on Culture Technology
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    • v.5 no.4
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    • pp.251-258
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    • 2019
  • This study proposed a system that can improve the employment rate and maintenance employment rate by filtering information related to employment in analyzing big data for students who want to find employment. The subject was a two-year female university, the existing employment strategy participated in the job search with simple information such as school grades and personality. As a result, the maintenance employment rate was relatively low due to the decrease in the satisfaction of students seeking employment and the incompatibility with the post-employment aptitude. In order to solve these problems, we propose a system that determines and filters whether the input data in the process of analyzing big data such as employment-related information to improve employment and maintenance employment rates.

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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An Analysis of Game Strategy and User Behavior Pattern Using Big Data: Focused on Battlegrounds Game (빅데이터를 활용한 게임 전략 및 유저 행동 패턴 분석: 배틀그라운드 게임을 중심으로)

  • Kang, Ha-Na;Yong, Hye-Ryeon;Hwang, Hyun-Seok
    • Journal of Korea Game Society
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    • v.19 no.4
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    • pp.27-36
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    • 2019
  • Approaches to find hidden values using various and enormous amount of data are on the rise. As big data processing becomes easier, companies directly collects data generated from users and analyzes as necessary to produce insights. User-based data are utilized to predict patterns of gameplay, in-game symptom, eventually enhancing gaming. Accordingly, in this study, we tried to analyze the gaming strategy and user activity patterns utilizing Battlegrounds in-game data to detect the in-game hack.

Analysis of Real Estate Market Trend Using Text Mining and Big Data (빅데이터와 텍스트마이닝을 이용한 부동산시장 동향분석)

  • Chun, Hae-Jung
    • Journal of Digital Convergence
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    • v.17 no.4
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    • pp.49-55
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    • 2019
  • This study is on the trend of real estate market using text mining and big data. The data were collected through internet news posted on Naver from August 2016 to August 2017. As a result of TF-IDF analysis, the frequency was high in the order of housing, sale, household, real estate market, and region. Many words related to policies such as loan, government, countermeasures, and regulations were extracted, and the region - related words appeared the most frequently in Seoul. The combination of the words related to the region showed that the frequencies of 'Seoul - Gangnam', 'Seoul - Metropolitan area', 'Gangnam - reconstruction' and 'Seoul - reconstruction' appeared frequently. It can be seen that the people's interest and expectation about the reconstruction of Gangnam area is high.

A Study on the Promotion of Yakseon Food Using Big Data

  • LEE, JINHO;KIM, AE SOOK;Hwang, Chi-Gon;Ryu, Gi Hwan
    • International Journal of Internet, Broadcasting and Communication
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    • v.14 no.4
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    • pp.41-46
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    • 2022
  • The purpose of this study is to confirm and analyze the impact on consumers through big data keyword analysis on weak food. For data collection, web documents, blogs, news, cafes, intellectuals, academic information, and Google Web, news, and Facebook provided by Naver and Daum were used as analysis targets. The data analysis period was set from January 2018 to December 2021. For data collection and analysis, the frequency and matrix of keywords were extracted through Textom, a social matrix site, and the relationship and connection centrality between keywords were analyzed and visualized using the Netdraw function among UCINET6 programs. In addition, CONCOR analysis was conducted to derive clusters for similar keywords. As a result of analyzing yakseon food with keywords, a total of 35,985 cases of collected data were derived. Through this, it was confirmed that medicinal food affects consumers. Furthermore, if a business model is created and developed through yakseon food, it will be possible to lead the popularization of yakseon food.

A Study on the General Public's Perceptions of Dental Fear Using Unstructured Big Data

  • Han-A Cho;Bo-Young Park
    • Journal of dental hygiene science
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    • v.23 no.4
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    • pp.255-263
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    • 2023
  • Background: This study used text mining techniques to determine public perceptions of dental fear, extracted keywords related to dental fear, identified the connection between the keywords, and categorized and visualized perceptions related to dental fear. Methods: Keywords in texts posted on Internet portal sites (NAVER and Google) between 1 January, 2000, and 31 December, 2022, were collected. The four stages of analysis were used to explore the keywords: frequency analysis, term frequency-inverse document frequency (TF-IDF), centrality analysis and co-occurrence analysis, and convergent correlations. Results: In the top ten keywords based on frequency analysis, the most frequently used keyword was 'treatment,' followed by 'fear,' 'dental implant,' 'conscious sedation,' 'pain,' 'dental fear,' 'comfort,' 'taking medication,' 'experience,' and 'tooth.' In the TF-IDF analysis, the top three keywords were dental implant, conscious sedation, and dental fear. The co-occurrence analysis was used to explore keywords that appear together and showed that 'fear and treatment' and 'treatment and pain' appeared the most frequently. Conclusion: Texts collected via unstructured big data were analyzed to identify general perceptions related to dental fear, and this study is valuable as a source data for understanding public perceptions of dental fear by grouping associated keywords. The results of this study will be helpful to understand dental fear and used as factors affecting oral health in the future.

A Big Data Analysis on Research Keywords, Centrality, and Topics of International Trade using the Text Mining and Social Network (텍스트 마이닝과 소셜 네트워크 기법을 활용한 국제무역 키워드, 중심성과 토픽에 대한 빅데이터 분석)

  • Chae-Deug Yi
    • Korea Trade Review
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    • v.47 no.4
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    • pp.137-159
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    • 2022
  • This study aims to analyze international trade papers published in Korea during the past 2002-2022 years. Through this study, it is possible to understand the main subject and direction of research in Korea's international trade field. As the research mythologies, this study uses the big data analysis such as the text mining and Social Network Analysis such as frequency analysis, several centrality analysis, and topic analysis. After analyzing the empirical results, the frequency of key word is very high in trade, export, tariff, market, industry, and the performance of firm. However, there has been a tendency to include logistics, e-business, value and chain, and innovation over the time. The degree and closeness centrality analyses also show that the higher frequency key words also have been higher in the degree and closeness centrality. In contrast, the order of eigenvector centrality seems to be different from those of the degree and closeness centrality. The ego network shows the density of business, sale, exchange, and integration appears to be high in order unlike the frequency analysis. The topic analysis shows that the export, trade, tariff, logstics, innovation, industry, value, and chain seem to have high the probabilities of included in several topics.

Keywords Analysis of Clothing Materials in Consumer Reviews Using Big Data Text Mining (빅데이터 텍스트 마이닝을 활용한 소비자 리뷰에서의 의류 소재 키워드 분석)

  • Gaeun Kang;Jiwon Park;Shinjung Yoo
    • Journal of the Korean Society of Clothing and Textiles
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    • v.48 no.4
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    • pp.729-743
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    • 2024
  • This research explores consumer preferences for materials in different clothing product categories, using web-crawling and text mining techniques. Specifically, the study focuses on the material-related terms found in consumer reviews across three distinct product categories: functional clothing, formal shirts, and knit sweaters. Top-selling products within each category were identified on the Naver Shopping website based on the volume of reviews, and the four most-reviewed products were selected. Six hundred reviews per product were analyzed using the Textom big-data analysis software to determine the frequency of material-related mentions and word associations. The analysis utilized two comparative metrics: product category and usage duration. Our findings reveal notable variations in the material preferences mentioned by consumers across different product categories. The study suggests a need to re-evaluate existing standardized review criteria to better reflect consumer interests specific to each product category. Additionally, an increase in material-related terms in reviews over one month indicates the potential importance of extending the duration of product reviews to enhance the accuracy of information that reflects longer-term consumer experiences with material quality.