• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.1-7
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• 2023

Since the first outbreak of COVID-19 in 2019, it has caused a huge blow to the restaurant industry. However, as social distancing was lifted as of April 2022, the restaurant industry gradually recovered, and as a result, interest in restaurant start-ups increased. Therefore, in this paper, big data analysis was conducted by selecting "restaurant start-up" as a key keyword through social media big data analysis using Textom and then conducting word frequency and CONCOR analysis. The collection period of keywords was selected from May 1, 2022 to May 23, 2023, after the lifting of social distancing due to COVID-19, and based on the analysis, the development of a restaurant start-up consulting chatbot service is proposed.

• 한국의상디자인학회지
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• 제24권3호
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• pp.1-15
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• 2022

The purpose of this study is to compare and analyze the perception of men's fashion before and after the COVID-19 pandemic. TEXTOM allowed the collection of Big Data based on the term 'men's fashion'. As for the data collection periods, Jan. 1, 2018 to Dec. 31, 2019 was set as the pre-COVID-19 era, while Jan. 1, 2020 to Dec. 31, 2021 was set as the post-COVID-19 era. The top 50 words in terms of appearance frequency were extracted from the data. The extracted words were processed using network centrality analysis and CONCOR analysis using Ucinet 6. Research findings were as follows. 1) In the pre-COVID-19 era, the appearance frequency of 'men' was the highest, followed by 'fashion', 'men's fashion', 'brand', 'daily look', 'suit', and 'department store'. These words came up with a high TF-IDF values. Network centrality analysis discovered that 'men', 'fashion', 'men's fashion', 'brand', and 'suit' had a high level of connectivity with other words. CONCOR analysis showed four significant groups: 'fashion item and styles', 'fashion show', 'purchase', and 'collection'. 2) In the post-COVID-19 era, the appearance frequency of 'men' was the highest, followed by 'fashion', 'brand', 'men's fashion', 'discount', 'women', and 'luxury'. These words also displayed high TF-IDF values. Network centrality analysis found that 'fashion', 'men', 'brand', 'men's fashion', and 'discount' had a high level of connectivity with other words. CONCOR analysis showed four significant groups: 'fashion item and style', 'fashion show', 'purchase', and 'situation'. 3) Before the outbreak of the pandemic, men were interested in suits to wear to the office, daily look, and fashion shows in Milan and Paris. They often purchased menswear in multi-brand and open stores. However, they were more interested in sneakers, casual styles, and online fashion shows as social distancing and working from home became common. Most purchased menswear through online platforms.

• 한국산학기술학회논문지
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• 제19권3호
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• pp.669-678
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• 2018

본 연구는 차량센싱데이터, 공공데이터 등 다종의 빅데이터를 활용하여 주행환경 분석 플랫폼 구축을 위한 정보기술 인프라를 개발하였다. 정보기술 인프라는 H/W 기술과 S/W 기술로 구분할 수 있다. 먼저, H/W 기술은 빅데이터 분산 처리를 위한 병렬처리 구조의 소형 플랫폼 서버를 개발하였다. 해당 서버는 1대의 마스터 노드와 9대의 슬래이브 노드로 구성하였으며, H/W 결함에 따른 데이터 유실을 막기 위하여 클러스터 기반 H/W 구성으로 설계하였다. 다음으로 S/W 기술은 빅데이터 수집 및 저장, 가공 및 분석, 정보시각화를 위한 각각의 프로그램을 개발하였다. 수집 S/W의 경우, 실시간 데이터는 카프카와 플럼으로 비실시간 데이터는 스쿱을 이용하여 수집 인터페이스를 개발하였다. 저장 S/W는 데이터의 활용 용도에 따라 하둡 분산파일시스템과 카산드라 DB로 구분하여 저장하는 인터페이스를 개발하였다. 가공 S/W는 그리드 인덱스 기법을 적용하여 수집데이터의 공간 단위 매칭과 시간간격 보간 및 집계를 위한 프로그램을 개발하였다. 분석 S/W는 개발 알고리즘의 탐재 및 평가, 장래 주행환경 예측모형 개발을 위하여 제플린 노트북 기반의 분석 도구를 개발하였다. 마지막으로 정보시각화 S/W는 다양한 주행환경 정보제공 및 시각화를 위하여 지오서버 기반의 웹 GIS 엔진 프로그램을 개발하였다. 성능평가는 개발서버의 메모리 용량과 코어개수에 따른 연산 테스트를 수행하였으며, 타 기관의 클라우드 컴퓨팅과도 연산성능을 비교하였다. 그 결과, 개발 서버에 대한 최적의 익스큐터 개수, 메모리 용량과 코어 개수를 도출하였으며, 개발 서버는 타 시스템 보다 연산성능이 우수한 것으로 나타났다.

• 한국조리학회지
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• 제23권8호
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• pp.195-205
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• 2017

The purpose of this study was to compare the Starbucks in South Korea with Starbucks in U.S.A through the semantic network analysis of big data by collecting online data with SCTM(Smart Crawling & Text Mining) program which was developed by big data research institute at Kyungsung University, a data collecting and processing program. The data collection period was from January 1st 2014 to December 7th 2017, and packaged Netdraw along with UCINET 6.0 were utilized for data analysis and visualization. After performing CONCOR(convergence of iterated correlation) analysis and centrality analysis, this study illustrated the current characteristics of Starbucks for Korea and U.S.A reflected by the social network and the differences between Korea and U.S.A. Since the Starbucks was greatly developed, especially in Korea. this study also was supposed to provide significant and social-network oriented suggestions for Starbucks USA, Starbucks Korea and also the whole coffee industry. Also this study revealed that big data analytics can generate new insights into variables that have been extensively studied in existing hospitality literature. In addition, implications for theory and practice as well as directions for future research are discussed.

• 동아시아경상학회지
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• 제10권2호
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• pp.105-113
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• 2022

Purpose - An increase in automation has been as a result of digital technologies. The data will be instrumental in the determination of the services that are more necessary so that more resources can be allocated for them. The purpose of the current research is to investigate how big data utilization will help increase the profitability in the industry 4.0 era. Research design, Data, and methodology - The present research has conducted the comprehensive literature content analysis. Quantitative approaches allow respondents to decide, but qualitative methods allow them to offer more information. In the next step, respondents are given data collection equipment, and information is collected. Result - The According to qualitative literature analysis, there are five ways in which big data utilization will help increase the profitability in the industry 4.0 era. The five solutions are (1) Better Customer Insight, (2) Increased Market Intelligence, (3) Smarter Recommendations and Audience Targeting, (4) Data-driven innovation, (5) Improved Business Operations. Conclusion - Modern companies have been seeking a competitive advantage so that they can have the edge over other companies in the same industries providing the same services and products. Big data is that technology that businesses have always wanted for an extended period of time to revolutionize their operations, making their businesses more profitable.

• 한국BIM학회 논문집
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• 제13권4호
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• pp.106-114
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• 2023

In this study, we developed a method to quantify urban social costs using mobile sensing data, providing a novel approach to urban planning. By collecting and analyzing extensive mobile data over time, we transformed travel patterns into measurable social costs. Our findings highlight the effectiveness of big data in urban planning, revealing key correlations between transportation modes and their associated social costs. This research not only advances the use of mobile data in urban planning but also suggests new directions for future studies to enhance data collection and analysis methods.

• 문화기술의 융합
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• 제8권2호
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• pp.209-215
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• 2022

본 연구의 목적은 '포스트 코로나 뉴노멀'에 관한 소셜 미디어(social media) 빅데이터를 분석하여 한국사회에서 '포스트코로나 뉴노멀'에 대한 대중 인식을 감성 측면에서 살펴봄으로서 포스트 코로나 시대를 선제적으로 대처하기 위한 기초자료를 제공하는 것이다. 자료 수집 및 분석을 위하여 빅데이터 분석 프로그램인 '텍스톰' (textom)의 감성분석 프로그램을 활용하였다. 데이터 수집기간은 2020년 10월 5일부터 2021년 10월 5일까지 1년이고, 수집 채널은 다음(daum)과 네이버(naver)의 블로그, 카페, 트위터 및 페이스북으로 설정하였다. 이 채널에서 수집된 총 3,770개의수집텍스트를 편집, 정제한 원문데이터가 본 연구를 위해 사용되었다. 분석의 결과는 다음과 같다. 첫째, '포스트 코로나 뉴노멀'에 대해 호감과 흥미 감성이 가장 높다. 즉 일상 회복과 기술 성장 및 새로워진 미래에 대한 기대 등 낙관적 감성이 77.62%로 주도적임을 알 수 있다. 둘째, 슬픔과 거부감 같은 부정 감성은 전체의 22.38%이나, 감성의 강도는 23.91%로 비율보다 높아 이 부정 감성이 강렬하다는 것을 시사한다. 본 연구는 '포스트 코로나 뉴노멀'에 대한 빅데이터 분석을 통해서 대중의 긍정 및 부정감성의 세부 요인분석의 기여도가 있다.

• 한국조리학회지
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• 제24권3호
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• pp.167-176
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• 2018

In this study, Big Data was used to collect the information related to 'Cooking Academy' keywords. After collecting all the data, we calculated the frequency through the text mining and selected the main words for future data analysis. Data collection was conducted from Google Web and News during the period from January 1, 2013 to December 31, 2017. The selected 64 words were analyzed by using UCINET 6.0 program, and the analysis results were visualized with NetDraw in order to present the relationship of main words. As a result, it was found that the most important goal for the students from cooking school is to work as a cook, likewise to have practical classes. In addition, we obtained the result that SNS marketing system that the social sites, such as Facebook, Twitter, and Instagram are actively utilized as a marketing strategy of the institute. Therefore, the results can be helpful in searching for the method of utilizing big data and can bring brand-new ideas for the follow-up studies. In practical terms, it will be remarkable material about the future marketing directions and various programs that are improved by the detailed curriculums through semantic network of cooking school by using big data.

• 한국전자통신학회논문지
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• 제15권4호
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• pp.695-702
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• 2020

최근 스마트 팜의 연구가 활발해지면서 시설하우스와 같은 실내 환경 제어는 높은 수준에 이르렀다. 그러나 노지에서 재배가 이루어지는 임업 분야에 ICT기술의 활용은 아직 미비한 실정이다. 본 논문에서는 ICT 기술을 적용한 LPWA 기반의 임산물 생육환경 수집 및 빅데이터 분석 시스템을 제안한다. 제안된 시스템은 oneM2M 아키텍처를 기반으로 구성하였으며 소규모 태양광 발전과 LPWA기술을 이용하여 노지에서 환경 데이터를 수집하여 서버에 전송한다. 전송된 데이터는 서버에서 빅 데이터로 구축되며 이를 활용해 임산물의 생산량과 품질을 예측한다. 제안된 시스템은 신재생 에너지와 스마트 팜의 융합을 통해 저비용, 고품질의 임산물 생산에 기여할 것으로 기대된다. 또한 노지에서 이루어지는 농작물의 생장 환경 모니터링과 oneM2M 아키텍처를 활용하는 타 산업 분야에 응용될 수 있다.

• 패션비즈니스
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• 제23권3호
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• pp.85-100
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• 2019

This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand's image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, "fashion shows," was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in "models" was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.