• 융합보안논문지
• /
• 제21권1호
• /
• pp.73-78
• /
• 2021

빅데이터는 초연결·초지능·초융합으로 대표되는 4차 산업혁명의 핵심 동력으로 이노베이션 창출, 비즈니스 모델 발굴을 위한 데이터 공유, 연계·활용이 중요하다. 그러나 빅데이터 플럇폼이 공유·연계를 고려하지 않고 폐쇄적으로 구축될 경우 양질의 풍부한 데이터 확보 및 활용이 어렵다. 따라서 본 논문은 데이터 생산·구축 및 연계·유통을 활성화 할 수 있도록 빅데이터 플랫폼의 다양한 사례를 비교·분석하여 빅데이터 플랫폼 인프라의 발전 방향을 제시한다.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2018년도 추계학술발표대회
• /
• pp.634-636
• /
• 2018

최근 들어 Big Data를 활용한 기술들이 많이 개발되고 있다. 본 연구에서는 Machine Learning과 Deep Learning을 이용하여 음성 Big Data를 활용한 이미지 시각화를 통해 보이스 피싱 등 여러 범죄에 도움이 되게 하고 그 외의 음성과 얼굴 매칭을 통한 새로운 보안시스템 및 다양한 시너지 효과들을 기대하는 서비스를 기술한다.

• 전기학회논문지
• /
• 제63권8호
• /
• pp.1111-1115
• /
• 2014

With the increasing interest of a demand side management using a Smart Grid infrastructure, the demand resources and energy usage data management becomes an important factor in energy industry. In addition, with the help of Advanced Measuring Infrastructure(AMI), energy usage data becomes a Big Data System. Therefore, it becomes difficult to store and manage the demand resources big data using a traditional relational database management system. Furthermore, not many researches have been done to analyze the big energy data collected using AMI. In this paper, we are proposing a Hadoop based Big Data system to manage the demand resources energy data and we will also show how the demand side management systems can be used to improve energy efficiency.

• International Journal of Internet, Broadcasting and Communication
• /
• 제14권3호
• /
• pp.91-100
• /
• 2022

Major developed countries are seriously considering smart factories to increase their manufacturing competitiveness. Smart factory is a customized factory that incorporates ICT in the entire process from product planning to design, distribution and sales. This can reduce production costs and respond flexibly to the consumer market. The smart factory converts physical signals into digital signals, connects machines, parts, factories, manufacturing processes, people, and supply chain partners in the factory to each other, and uses the collected data to enable the smart factory platform to operate intelligently. Enhancing personalized value is the key. Therefore, it can be said that the success or failure of a smart factory depends on whether big data is secured and utilized. Standardized communication and collaboration are required to smoothly acquire big data inside and outside the factory in the smart factory, and the use of big data can be maximized through big data analysis. This study examines big data analysis and standardization in smart factory. Manufacturing innovation by country, smart factory construction framework, smart factory implementation key elements, big data analysis and visualization, etc. will be reviewed first. Through this, we propose services such as big data infrastructure construction process, big data platform components, big data modeling, big data quality management components, big data standardization, and big data implementation consulting that can be suggested when building big data infrastructure in smart factories. It is expected that this proposal can be a guide for building big data infrastructure for companies that want to introduce a smart factory.

• 한국의류학회지
• /
• 제42권1호
• /
• pp.148-158
• /
• 2018

This study analyzes recent trends in fashion retailing instigated by the fourth industrial revolution and approaches the trends in terms of the convergence of big data and artificial intelligence. The findings are as below. First, companies like 'Edited' and 'Stylumia' offer solutions that support the strategic decisions of fashion brands and fashion retailers by analyzing big data using artificial intelligence. Second, the convergence of big data and artificial intelligence scales personalized service on the web as examples of 'Coded Couture', 'StitchFix', and 'Thread'. Third, the insights gained from artificial intelligence and big data help create new fashion retailing platforms such as 'Botshop' and 'Lyst'. Last, artificial intelligence and big data assist with design. 'Ivyrevel' designs digital fashion, assisted by a macroscopic perspective on fashion trends, market and consumers through the analysis of big data. The Fourth Industrial Revolution brings changes across all industries that will likely accelerate. The fashion industry is also undergoing many changes with advancements in scientific technology. The convergence of big data and artificial intelligence will play a key role in the future of fast-moving industry like fashion, where fickle tastes of consumers are the main drivers.

• 한국응용과학기술학회지
• /
• 제37권1호
• /
• pp.76-82
• /
• 2020

Big data analysis in healthcare research seems to be a necessary strategy for the convergence of sports science and technology in the era of the Fourth Industrial Revolution. The purpose of this study is to provide the basic review to secure the diversity of big data and healthcare convergence by discussing the concept, analysis method, and application examples of big data and by exploring the application. Text mining, data mining, opinion mining, process mining, cluster analysis, and social network analysis is currently used. Identifying high-risk factor for a certain condition, determining specific health determinants for diseases, monitoring bio signals, predicting diseases, providing training and treatments, and analyzing healthcare measurements would be possible via big data analysis. As a further work, the big data characteristics provide very appropriate basis to use promising software platforms for development of applications that can handle big data in healthcare and even more in sports science.

• 식품보건융합연구
• /
• 제10권1호
• /
• pp.1-6
• /
• 2024

Purpose: The research aims to address the intricacies of AI and Big Data application within the food industry. This study explores the strategic implementation of AI and Big Data in the food industry. The study seeks to understand how these technologies can be employed to bolster consumer engagement and contribute to market expansion, while considering ethical implications. Research Method: This research employs a comprehensive approach, analyzing current trends, case studies, and existing academic literature. It focuses on the application of AI and Big Data in areas such as supply chain management, consumer behavior analysis, and personalized marketing strategies. Results: The study finds that AI and Big Data significantly enhance market analytics, consumer personalization, and market trend prediction. It highlights the potential of these technologies in creating more efficient supply chains, improving consumer satisfaction through personalization, and providing valuable market insights. Conclusion and Implications: The paper offers actionable insights and recommendations for the effective implementation of AI and Big Data strategies in the food industry. It emphasizes the need for ethical considerations, particularly in data privacy and the transparency of AI algorithms. The study also explores future trends, suggesting that AI and Big Data will continue to revolutionize the industry, emphasizing sustainability, efficiency, and consumer-centric practices.

• 한국산업융합학회 논문집
• /
• 제22권6호
• /
• pp.749-756
• /
• 2019

Big data is structured and unstructured data that is so difficult to collect, store, and so on due to the huge amount of data. Many institutions, including universities, are building student convergence systems to foster talents for data science and AI convergence, but there is an absolute lack of research on what kind of education is needed and what kind of education is required for students. Therefore, in this paper, after conducting the correlation analysis based on the questionnaire on basic surveys and courses to improve the curriculum by grasping the satisfaction and demands of the participants in the "2019 Big Data Youth Talent Training Course" held at K University, Regression analysis was performed. As a result of the study, the higher the satisfaction level, the satisfaction with class or job connection, and the self-development, the more positive the evaluation of program efficiency.

• 디지털융복합연구
• /
• 제11권4호
• /
• pp.267-271
• /
• 2013

우리사회는 점점 더 융/복합 현상이 가속화되고, 광범위한 영역으로 확대되고 있다. 이러한 중심축에는 정보통신 기술이 자리잡고 있음은 당연한 일이다. 일례로 정보통신기술과 의료산업의 융합의 결과로 스마트 헬스케어 산업이 등장하였으며, 모든 분야에 정보통신 기술을 접목하고자 하는 노력들이 계속되고 있다. 이로 인해 우리주변에는 수많은 디지털 데이터들이 만들어지고 있다. 또 다른 한편으로는 대중화 되고 있는 스마트폰, 태블릿PC와 카메라, 게임기기등을 통하여 다양한 데이터들이 생성되고 있다. 본 연구에서는 광범위하게 발생하고 있는 빅데이터에 대한 활용 상태를 알아보고 빅데이터 플랫폼의 한 축인 처리 프로세스들에 대해 비교, 분석하였다.

• 한국융합학회논문지
• /
• 제8권5호
• /
• pp.29-35
• /
• 2017

우리나라는 최근 10여 년간 정보통신기술을 중심으로 융합에 대한 높은 관심을 보이고 있으나, 방송과 통신 분야의 성공적인 융합으로 IPTV 등의 성공적인 융합사례 일부 분야를 제외하면, 타 분야 정보통신기술의 융합을 통해 국민, 시민이 체감할 수 있는 성과는 제한적이다. 또한, 사물 인터넷과 빅데이터의 결합으로 서비스 이용자를 둘러싼 자연과 사회 환경에서의 무한한 데이터가 생성되고 활용되어, 보다나은 서비스를 창출할 수 있을 것으로 나타났으나 부처 및 부서 간 칸막이, 정보의 연계 미흡, 정보통신기술 융합을 촉진할 수 있는 정책 제도의 한계점 등의 문제점으로 융합산업 육성에 한계를 보이고 있다. 그러므로 본 논문에서는 사물 인터넷과 빅데이터의 융합을 통한 신산업 창출 추진하기 위해 저해효소는 무엇인지 현황조사를 통해 문제점을 도출하며 문제점 해결 및 사물 인터넷과 빅데이터 활성화를 위한 기술개발, 부가가치 서비스 창출을 위한 추진방안 도출 등의 개선방안을 제시하고, 관련 기술의 융합 활성화를 위한 정책제언 및 활용방안을 제시한다.