• 제목/요약/키워드: Engineering Big Data

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4차 산업혁명 시대에 적합한 빅데이터 대학 교육과정 연구 (Research on big data curriculum in university suitable for the era of the 4th industrial revolution)

  • Choi, Hun;Kim, Gimun
    • 한국정보통신학회논문지
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    • 제24권11호
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    • pp.1562-1565
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    • 2020
  • With the development of digital technology, the industrial structure is becoming digitalize. The government selected big data as the key technology of the 4th industrial revolution. Among them, big data is widely used to create new values and services by utilizing vast amounts of information. In order to cultivate professional manpower for the use of big data, various education programs are provided at universities. We intend to develop a curriculum for systematic training of talented people who can acquire knowledge about the three stages of collection, analysis, and application of big data. To this end, subjects are classified into basic competency, technical competency, analysis competency, and business competency based on the big data competency model proposed by the Korea Internet & Security Agency.

공간빅데이터 개념 및 체계 구축방안 연구 (Study for Spatial Big Data Concept and System Building)

  • 안종욱;이미숙;신동빈
    • Spatial Information Research
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    • 제21권5호
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    • pp.43-51
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    • 2013
  • 본 연구에서는 최근 이슈가 되고 있는 공간빅데이터에 대한 개념과 효과적으로 공간빅데이터체계를 구축하기 위한 방안을 제시하였다. 공간빅데이터는 3V(volume, variety, velocity)로 정의되고 있는 빅데이터를 6V(volume, variety, velocity, value, veracity, visualization)의 빅데이터로 진화시키는 기반이라 할 수 있다. 공간빅데이터를 효과적으로 구축하기 위해서는 공간빅데이터체계 구축으로 추진되어야 하며, 공간빅데이터체계는 국가공간정보기반, 융합플랫폼, 서비스제공자, 생산요소제공자로서의 역할을 수행해야 한다. 이러한 공간빅데이터체계의 구성요소는 인프라(하드웨어), 기술(소프트웨어), 공간빅데이터(데이터), 인력, 법 제도 등이며, 공간빅데이터체계 구축을 위한 목표로 공간기반 정책수립 지원, 공간빅데이터 플랫폼 기반 산업활성화, 공간 빅데이터 융합기반 조성, 공간관련 사회현안의 적극적 해결로 제시하였다. 그리고 목표에 대한 추진전략은 범정부적 협력체계 구축, 신산업 창출 및 활용 활성화, 성과활용 중심의 공간빅데이터 플랫폼 구축, 공간빅데이터 관련 기술경쟁력 확보로 제시하였다.

빅데이터 역량 평가를 위한 참조모델 및 수준진단시스템 개발 (An Assessment System for Evaluating Big Data Capability Based on a Reference Model)

  • 천민경;백동현
    • 산업경영시스템학회지
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    • 제39권2호
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    • pp.54-63
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    • 2016
  • As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

공학교육 빅 데이터 분석 도구 개발 연구 (Research on the Development of Big Data Analysis Tools for Engineering Education)

  • 김윤영;김재희
    • 공학교육연구
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    • 제26권4호
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    • pp.22-35
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    • 2023
  • As information and communication technology has developed remarkably, it has become possible to analyze various types of large-volume data generated at a speed close to real time, and based on this, reliable value creation has become possible. Such big data analysis is becoming an important means of supporting decision-making based on scientific figures. The purpose of this study is to develop a big data analysis tool that can analyze large amounts of data generated through engineering education. The tasks of this study are as follows. First, a database is designed to store the information of entries in the National Creative Capstone Design Contest. Second, the pre-processing process is checked for analysis with big data analysis tools. Finally, analyze the data using the developed big data analysis tool. In this study, 1,784 works submitted to the National Creative Comprehensive Design Contest from 2014 to 2019 were analyzed. As a result of selecting the top 10 words through topic analysis, 'robot' ranked first from 2014 to 2019, and energy, drones, ultrasound, solar energy, and IoT appeared with high frequency. This result seems to reflect the current core topics and technology trends of the 4th Industrial Revolution. In addition, it seems that due to the nature of the Capstone Design Contest, students majoring in electrical/electronic, computer/information and communication engineering, mechanical engineering, and chemical/new materials engineering who can submit complete products for problem solving were selected. The significance of this study is that the results of this study can be used in the field of engineering education as basic data for the development of educational contents and teaching methods that reflect industry and technology trends. Furthermore, it is expected that the results of big data analysis related to engineering education can be used as a means of preparing preemptive countermeasures in establishing education policies that reflect social changes.

Advanced Big Data Analysis, Artificial Intelligence & Communication Systems

  • Jeong, Young-Sik;Park, Jong Hyuk
    • Journal of Information Processing Systems
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    • 제15권1호
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    • pp.1-6
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    • 2019
  • Recently, big data and artificial intelligence (AI) based on communication systems have become one of the hottest issues in the technology sector, and methods of analyzing big data using AI approaches are now considered essential. This paper presents diverse paradigms to subjects which deal with diverse research areas, such as image segmentation, fingerprint matching, human tracking techniques, malware distribution networks, methods of intrusion detection, digital image watermarking, wireless sensor networks, probabilistic neural networks, query processing of encrypted data, the semantic web, decision-making, software engineering, and so on.

e-Commerce 상에서 빅데이터 서비스제공 기대가 이용의도에 미치는 영향 연구 (A Study on the Influence of Expectation of Big Data Service on e-Commerce on the Use Intension)

  • 김영국;염수환;김진형;배석민;정재진
    • 한국멀티미디어학회논문지
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    • 제22권9호
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    • pp.1132-1139
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    • 2019
  • Big data is prominently used as a prediction method in achieving a goal, because it can analyze the regularities to predict future results from a vast amount of past data. Furthermore, big data has huge influence in very diverse academic fields. On such awareness, this study analyzed the regular effect of e-Commerce usefulness from the effects which expectations on big-data service affect the usage purpose of e-Commerce usefulness. This study categorized e-Commerce usefulness into quality recognition, service, and ease, and studied how each category works between the relationship of big-data service expectation and the use intention.

공간 빅데이터 서비스 활성화를 위한 정책과제 도출 (Deduction of the Policy Issues for Activating the Geo-Spatial Big Data Services)

  • 박준민;이명호;신동빈;안종욱
    • Spatial Information Research
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    • 제23권6호
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    • pp.19-29
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    • 2015
  • 본 연구는 공간 빅데이터 서비스 활성화를 위한 정책과제 도출을 목적으로 수행하였다. 이를 위해 관련 선행연구를 검토하고, 국내 외 공간 빅데이터 관련 추진체계 및 정책현황을 분석하였다. 그 결과 미래 공간정보 융 복합 대응정책 미흡, 개인정보 보호 및 서비스 활성화 제도적 기반 미흡, 관련 기술 정책 마련 미흡, 공간 빅데이터 구축 활용을 위한 추진체계 미흡, 공공정보의 품질저하와 공유체계 미흡 등의 문제점이 도출되었다. 다음으로 도출된 문제점을 해결하기 위해 정책 추진방향을 설정하고, 공간 빅데이터 추진체계 마련, 관련 법 제도 개선, 공간 빅데이터 관련 기술 개발, 공간 빅데이터 지원 사업 추진, 공공DB 융 복합 공유체계 마련 총 5가지의 정책과제를 제시하였다.

Building Smarter City through Big Data - Best Practices in Seoul Metropolitan Gov.

  • Kim, Ki-Byoung
    • 국제학술발표논문집
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    • The 6th International Conference on Construction Engineering and Project Management
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    • pp.19-20
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    • 2015
  • Since 2013, Seoul Metropolitan Government (SMG) has introduced big data initiatively in administration and put into practices in transportation, safety, welfare in order to overcome limited resources and conflicting interests. For establishing a new midnight bus service, SMG prepared optimized midnight bus routes by analyzing big data from mobile phone Call Data Record (CDR) through collaboration with a telecommunication company. Despite of limited budget and resources, newly identified routes can cover over 42% of the citizen with 9 routes and less than 1% of buses compare with day time operation. In addition to solve transportation problem, SMG utilizes big data to resolve location selection problem for choosing new facility locations such as life double cropping centers and senior citizen leisure centers. As results, SMG demonstrates big data as a good tool to make policies and to build smarter city by overcome space-time limitation of resources, mediation of conflicts, and maximizes benefit of the citizen.

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Comparative Analysis of Centralized Vs. Distributed Locality-based Repository over IoT-Enabled Big Data in Smart Grid Environment

  • Siddiqui, Isma Farah;Abbas, Asad;Lee, Scott Uk-Jin
    • 한국컴퓨터정보학회:학술대회논문집
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    • 한국컴퓨터정보학회 2017년도 제55차 동계학술대회논문집 25권1호
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    • pp.75-78
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    • 2017
  • This paper compares operational and network analysis of centralized and distributed repository for big data solutions in the IoT enabled Smart Grid environment. The comparative analysis clearly depicts that centralize repository consumes less memory consumption while distributed locality-based repository reduce network complexity issues than centralize repository in state-of-the-art Big Data Solution.

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실시간 데이터 분석을 위한 컨테이너 기반 가상화 성능에 관한 연구 (A Study on Performance Evaluation of Container-based Virtualization for Real-Time Data Analysis)

  • 최보아;한재덕;오다솜;박현국;김현아;서민관;이종혁
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2020년도 춘계학술발표대회
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    • pp.32-35
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    • 2020
  • 본 논문은 실시간 데이터 분석을 위한 컨테이너 가상화 기술 사용에 대한 효용성을 알아보기 위해 HDP 와 MapR 배포판에 포함된 Spark 를 도커라이징 전과 후 환경에 설치 후 HiBench 벤치마크 프로그램을 이용해 성능을 측정하였다. 그리고 성능 측정치에 대해 대응표본 t 검정을 이용하여 도커라이징 전과 후의 성능 차이가 있는지를 통계적으로 분석하였다. 분석 결과, HDP 는 도커라이징 전과 후에 대한 성능 차이가 있었지만 MapR 은 성능 차이가 없었다.