메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술대회자료
저자정보
Muhammad Aslam Jarwar (Hankuk University of Foreign Studies) Sajjad Ali (Hankuk University of Foreign Studies) Ilyoung Chong (Hankuk University of Foreign Studies)
저널정보
한국통신학회 한국통신학회 학술대회논문집 2019년도 한국통신학회 동계종합학술발표회 논문집
발행연도
2019.1
수록면
640 - 643 (4page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
During the production, distribution, and consumption of energy, a large quantity of data is generated. For efficiently using of energy resources other supplementary data such as building information, weather, and environmental data etc. are also collected and used. All these energy data and relevant data is published as linked data in order to enhance the reusability of data and maximization of energy management services capability. However, the quality of this linked data is questionable because of wear and tears of sensors, unreliable communication channels, and highly diversification of data sources. The provision of high-quality energy management services requires high quality linked data, which reduces billing cost and improve the quality of the living environment. Assessment and improvement methodologies for the quality of data along with linked data needs to process very diverse data from highly diverse data sources. Microservices based data-driven architecture has great significance to processes highly diverse linked data with modularity, scalability, and reliability. This paper proposed microservices based architecture along with domain data and metadata ontologies to enhance and assess energy-related linked data quality.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. LINKED DATA QUALITY DIMENSIONS AND METRICS
IV. LINKED DATA QUALITY DIMENSIONS WITH RESPECT TO ENERGY DATA
V. MICROSERVICES BASED ARCHITECTURE TO PROCESS ENERGY LINKED DATA QUALITY FOR SERVICES
VI. ONTOLOGY FOR ENERGY LINKED DATA QUALITY MODEL
VII. CONCLUSION
REFERENCES

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2019-567-000535876