RISS 학술연구정보서비스

검색

인기 검색어

    다국어 입력

    http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

    변환된 중국어를 복사하여 사용하시면 됩니다.

    예시)
    • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
    • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
    닫기
    KCI등재

    스마트공장을 위한 빅데이터 애널리틱스 플랫폼 아키텍쳐 개발 = Developing a Big Data Analytics Platform Architecture for Smart Factory

    한글로보기

    https://www.riss.kr/link?id=A104983464

    • 0

      상세조회
    • 0

      다운로드
    서지정보 열기
    • 내보내기
    • 내책장담기
    • 공유하기
    • 오류접수

    부가정보

    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.
    번역하기

    While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Fa...

    While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

    더보기

    참고문헌 (Reference)

    1 송영미, "도로 침수영역의 탐색을 위한 빅데이터 분석 시스템 연구" 한국멀티미디어학회 18 (18): 925-934, 2015

    2 S.H. Baek, "The-state-of-the art and Standardization Strategies for Smart Manufacturing" KISTEP 2016

    3 ECMA International, "The JSON Data Interchange Format, ECMA-404"

    4 KDB, "The Feasibility of Smart Factory for Advancing Korean Manufacturing Industry, KDB Industrial Issue Paper" KDB 2015

    5 G.T. Lee, "Technology Trends of Smart Factory" KEIT 2015

    6 KIAT, "Research Trend of Smart Manufacturing in U.S.A" KIAT 2015

    7 "PMML 4.2–General Structure" Data Mining Group

    8 MTConnect Institute, "MTConnect® Standard Part 1–Overview and Protocol"

    9 "KNIME Analytics Platform"

    10 KOSF, "Industrial Reference Models for Smart Factory Propagation" KOSF 2015

    1 송영미, "도로 침수영역의 탐색을 위한 빅데이터 분석 시스템 연구" 한국멀티미디어학회 18 (18): 925-934, 2015

    2 S.H. Baek, "The-state-of-the art and Standardization Strategies for Smart Manufacturing" KISTEP 2016

    3 ECMA International, "The JSON Data Interchange Format, ECMA-404"

    4 KDB, "The Feasibility of Smart Factory for Advancing Korean Manufacturing Industry, KDB Industrial Issue Paper" KDB 2015

    5 G.T. Lee, "Technology Trends of Smart Factory" KEIT 2015

    6 KIAT, "Research Trend of Smart Manufacturing in U.S.A" KIAT 2015

    7 "PMML 4.2–General Structure" Data Mining Group

    8 MTConnect Institute, "MTConnect® Standard Part 1–Overview and Protocol"

    9 "KNIME Analytics Platform"

    10 KOSF, "Industrial Reference Models for Smart Factory Propagation" KOSF 2015

    11 KSA, "Global Trends and Korean Standardization Strategies for Smart Factory" KSA 2015

    12 S. Kotsiantis, "Data Preprocessing for Supervised Leaning" 1 (1): 111-117, 2006

    13 W. Shen, "Applications of Agent-based Systems in Intelligent Manufacturing: an Updated Review" 20 (20): 415-431, 2006

    14 L. Monostori, "Agent-based Systems for Manufacturing" 55 (55): 697-720, 2006

    15 A. Pavlo, "A Comparison of Approaches to Large-scale Data Analysis" 165-178, 2009

    16 Deloitte, "2016 Global Manufacturing Competitiveness" 2015

    더보기

    동일학술지(권/호) 다른 논문

    분석정보

    View

    상세정보조회

    0

    Usage

    원문다운로드

    0

    대출신청

    0

    복사신청

    0

    EDDS신청

    0

    동일 주제 내 활용도 TOP

    더보기

    주제

    연도별 연구동향

    연도별 활용동향

    연관논문

    연구자 네트워크맵

    공동연구자 (7)

    유사연구자 (20) 활용도상위20명

    인용정보 인용지수 설명보기

    학술지 이력

    학술지 이력
    연월일 이력구분 이력상세 등재구분
    2026 평가 재인증평가 신청대상 (재인증)
    2020-01-01 등재 등재학술지 유지 (재인증) KCI등재
    2017-01-01 등재 등재학술지 유지 (계속평가) KCI등재
    2013-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2010-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2008-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2005-01-01 등재 등재학술지 선정 (등재후보2차) KCI등재
    2004-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
    2002-01-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
    더보기

    학술지 인용정보

    학술지 인용정보
    기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
    2016 0.61 0.61 0.56
    KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
    0.49 0.44 0.695 0.15
    더보기

    이 자료와 함께 이용한 RISS 자료

    나만을 위한 추천자료

    해외이동버튼