• 대한안전경영과학회지
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• 제15권4호
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• pp.289-294
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• 2013

Due to sudden transition to intellectual society corresponding with fast technology progress, companies and nations need to focus on development and guarantee of intellectual property. The possession of intellectual property has been the important factor of competition power. In this paper we developed the efficient patent search process with big data analysis tool R. This patent search process consists of 5 steps. We result that at first this process obtain the core patent search key words and search the target patents through search formula using the combination of above patent search key words.

• Journal of the Korean Data and Information Science Society
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• 제27권4호
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• pp.1075-1081
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• 2016

The big data analysis has received much attention from the researchers working in various fields because the big data has a great potential in detecting or predicting future events such as epidemic outbreaks and changes in stock prices. Reflecting the current popularity of big data analysis, many authors have proposed methods tracking influenza epidemics based on internet-based information. The recently proposed 'autoregressive model using Google (ARGO) model' (Yang et al., 2015) is one of those influenza tracking models that harness search queries from Google as well as the reports from the Centers for Disease Control (CDC), and appears to outperform the existing method such as 'Google Flu Trends (GFT)'. Although the ARGO predicts well the outbreaks of influenza, this study demonstrates that a classical seasonal autoregressive integrated moving average (SARIMA) model can outperform the ARGO. The SARIMA model incorporates more accurate seasonality of the past influenza activities and takes less input variables into account. Our findings show that the SARIMA model is a functional tool for monitoring influenza epidemics.

• 한국전자통신학회논문지
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• 제17권1호
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• pp.161-166
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• 2022

본 논문에서는 데이터 분석 도구인 R을 이용하여 빅데이터 분석을 수행할 때 발생하는 문제점을 분석하고, 빅데이터의 분산 처리를 효과적으로 지원하는 스파크와 R을 연계한 SparkR을 이용한 분석의 유용성을 제시하고자 한다. 먼저, 대량의 데이터를 로딩하고 연산을 수행할 때 발생하는 R의 메모리 할당 문제점과 R과 비교한 SparkR의 특징 및 프로그래밍 환경을 분석한다. 그리고, 선형 회귀 분석을 각각의 환경에서 수행할 때의 실행 성능을 비교 분석한다. 분석 결과 SparkR을 통해 추가적인 언어 학습 없이도 R을 그대로 이용하여 데이터 분석에 활용할 수 있음을 보였으며, SparkR을 이용하여 R로 작성된 코드를 클러스터 내 노드 수의 증가에 따라 효과적으로 분산 처리할 수 있었다.

• 한국병원경영학회지
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• 제23권4호
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• pp.26-39
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• 2018

Purposes: The purpose of this study is to analyze the issue of interest in patient medical service of small and medium hospitals using big data. Methods: The method of this study was implemented by data mining and social network using SNS big data. The analysis tool were extracted key keywords and analyzed correlation by using Textom, Ucinet6 and NetDraw program. Findings: In the results of frequency, the network-centered and closeness centrality analysis, It was shown that the government center is interested in the major explanations and evaluations of the technology, information, security, safety, cost and problems of small and medium hospitals, coping with infections, and actual involvement in bank settlement. And, were extracted care for disabilities such as pediatrics, dentistry, obstetrics and gynecology, dementia, nursing, the elderly, and rehabilitation. Practical Implications: Future studies will be more useful if analyzed the needs of customers for medical services in the metropolitan area and provinces may be different in the small and medium hospitals to be studied, further classification studies.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.179-184
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• 2024

Franchise are now a red ocean in Food industry and they need to find other options to appeal for their product, the uprising content, food tech. The franchises are working on R&D to help franchisees with the operations. Through this paper, we analyze the franchise interest on food tech and to help find the necessity of development for franchisees who are in needs with hand, not of human, but of technology. Using Textom, a big data analysis tool, "franchise" and "food tech" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 01 January, 2023 to 31 December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "food tech" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, a total of 10,049 words were derived, and among them, the top 50 keywords with the highest relevance and search frequency were selected and applied to this study. The top 50 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. By using big data analysis, it was found out that franchise do have interest on food tech. "technology", "franchise", "robots" showed many interests and keyword "R&D" showed that franchise are keen on developing food tech to seize competitiveness in Franchise Industry.

• 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.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.327-331
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• 2023

This paper proposes a tourist destination recommendation application that combines a chatbot and a recommendation system. The data to be entered into the chatbot was through big data on social media. Through TEXTOM, a total of 22,701 data were collected over a one-year period from January 2022 to January 2023. Non-terms that interfere with analysis were removed through the data purification process. Using refined data, network visualization and CONCOR analysis were used to identify the information users want to obtain about travel to Jeju Island, and categories for each cluster were organized. The content was intuitively organized so that even those who approached it for the first time could easily use it, reducing the difficulty of operating the application. In this paper, users can select their own preferences and receive information. In addition, a tool called a chatbot allows users to focus more on the process of acquiring information by gaining a sense of reality while operating the application. This suggests an application that can reach the purpose of the curator by affecting the user's desire to visit tourist attractions.

• 기술혁신학회지
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• 제22권2호
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• pp.265-281
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• 2019

경제가 성장하고 인터넷이 발전되면서 사람들의 경제형태와 소비는 많이 바뀌었다. 중국 알리바바 그룹은 모바일, 온라인, 오프라인, 인공지능을 결합한 플랫폼으로 약 28조의 매출을 창출하고 있다. 이는 1초에 약 25만건을 처리하는 수준이며, 2016년 대비 40% 증가했다. 이를 가능하게 한 핵심 기술은 소위 Data Technology라고 불리는 빅데이터와 클라우드 컴퓨팅이 융합된 기술이다. 기술의 발전속도에 비해 Data Technology에 관한 정확한 개념적 정의는 부족하다. 이에 본 논문은 빅데이터 분석기법인 TexTom을 활용하여 구글과 네이버의 최근 3개년(2015년 11월~2018년 11월) 신문기사를 데이터 마이닝 및 정제하여 'Data Technology' 키워드로 한정하여 관련 핵심 키워드를 도출하였다. 그 결과 빅데이터, O2O, 인공지능, 사물인터넷, 클라우드 컴퓨팅의 핵심 키워드 기술이 Data Technology와 관계가 있음을 알수 있었다. 본 연구의 분석결과는 향후 Data Technology 시대가 도래되면 참고할 수 있는 유용한 정보로 활용될 수 있다.

• 한국인터넷방송통신학회논문지
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• 제20권3호
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• pp.163-170
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• 2020

정보통신기술의 발전과 함께 데이터의 생산량이 기하급수적으로 증가하면서 빅데이터에 대한 관심이 높아지고 있다. 빅데이터 관련 기술들도 발전함에 따라 여러 분야에서 빅데이터가 수집, 저장, 처리, 분석, 활용되고 있다. 특히 보건의료 분야에서의 빅데이터 분석은 사회경제적으로도 큰 영향력을 발휘할 수 있기 때문에 큰 주목을 받고 있다. 빅데이터 기술을 환자 진단 데이터 분석에 활용하여 간단한 병원 진료에 투여되는 막대한 비용을 절감할 수 있을 것으로 전망된다. 따라서 본 논문에서는 환자 데이터를 분석하여 병원에 가기 어려운 환자나 의학적인 전문 지식이 없는 간병인들에게 의사의 진단과 가까운 간병 가이드 정보를 제시하고자 한다. 먼저 수집된 환자 데이터를 HDFS에 저장하고, 하둡 환경에서 빅데이터 처리 및 분석 도구인 R을 이용하여 데이터를 처리한 후 분류분석을 한다. R의 다양한 기능들을 웹에 구현하기 위해 활용되는 R Shiny를 이용하여 웹 서버에 시각화를 한다.

• 한국교육논총
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• 제43권1호
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• pp.1-27
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• 2022

세계적으로 코로나19 바이러스가 만연해짐에 따라 다양한 분야에서 비대면화를 시행하게 되었고, 교육 시스템 또한 급격한 비대면화로 인해 많은 관심이 집중되기 시작하였다. 본 연구의 목적은 현재까지 계속적으로 변화하고 있는 교육환경에 맞추어 비대면 교육이 나아가야 하는 방향성에 대해서 분석하는 것이다. 본 연구에서는 다양한 의견들이 존재하는 소셜네트워크 빅데이터를 수집하기 위하여 텍스톰(Textom), 유씨넷6(Ucinet6) 분석 도구 프로그램을 사용하여 데이터를 시각화하였다. 연구 결과 '코로나'와 관련된 키워드가 주를 이루었으며 '기사', '뉴스'등의 높은 빈도의 키워드들이 존재했다. 분석 결과 네트워크 장애 및 보안 문제와 같은 비대면 교육에 관련한 다양한 이슈들을 확인해 볼 수 있었고, 분석 이후 교육 시장의 성장과 교육 환경의 변화에 따른 비대면 교육 시스템의 방향성에 관하여 연구하였다. 또한 빅데이터를 이용하여 분석한 비대면 교육시의 보안 강화 필요성과 수업 방식에 대한 피드백의 필요성이 존재한다.