The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
조회수
87
현재 데이터 과학 시대에 데이터 품질은 비즈니스 운영에 중요하고 결정적인 영향을 미칩니다. 이는 기상학 분야에서 접하는 기상자료의 경우에도 다르지 않습니다. 그러나 기존의 기상 데이터 품질 관리 방법은 주로 오류 감지 및 null 값 감지에 중점을 둡니다. 즉, 데이터 출력 결과만 고려하고 작업 흐름에서 발생할 수 있는 품질 문제는 무시합니다. 이러한 문제를 해결하기 위해 본 논문에서는 특히 데이터 웨어하우징의 체계적 특성과 프로세스 중심 요구를 고려하여 TQM(Total Quality Management) 관점을 기반으로 하는 TMDQ(Total Meteorological Data Quality) 프레임워크를 제안합니다. 실제 적용에서 이 논문은 제안된 프레임워크를 기상 관측자가 시기적절하고 효율적인 방식으로 기상 데이터의 품질을 개선하고 유지하는 데 도움이 되는 시스템 개발의 기초로 사용합니다. 제안된 프레임워크의 타당성을 검증하고 그 기능과 사용법을 입증하기 위해 대만의 Tamsui Meteorological Observatory(TMO)에서 구현되었습니다. 제안된 프레임워크를 통해 구축된 4가지 품질 차원 지표는 기상 관측자가 기상 데이터의 다양한 특성을 다양한 측면에서 파악하는 데 도움이 될 것입니다. 제안된 프레임워크의 적용 및 연구 제한 사항에 대해 논의하고 향후 연구를 위한 가능한 방향을 제시합니다.
Wen-Lung TSAI
OIT
Yung-Chun CHAN
OIT
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Wen-Lung TSAI, Yung-Chun CHAN, "Designing a Framework for Data Quality Validation of Meteorological Data System" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 4, pp. 800-809, April 2019, doi: 10.1587/transinf.2018DAP0021.
Abstract: In the current era of data science, data quality has a significant and critical impact on business operations. This is no different for the meteorological data encountered in the field of meteorology. However, the conventional methods of meteorological data quality control mainly focus on error detection and null-value detection; that is, they only consider the results of the data output but ignore the quality problems that may also arise in the workflow. To rectify this issue, this paper proposes the Total Meteorological Data Quality (TMDQ) framework based on the Total Quality Management (TQM) perspective, especially considering the systematic nature of data warehousing and process focus needs. In practical applications, this paper uses the proposed framework as the basis for the development of a system to help meteorological observers improve and maintain the quality of meteorological data in a timely and efficient manner. To verify the feasibility of the proposed framework and demonstrate its capabilities and usage, it was implemented in the Tamsui Meteorological Observatory (TMO) in Taiwan. The four quality dimension indicators established through the proposed framework will help meteorological observers grasp the various characteristics of meteorological data from different aspects. The application and research limitations of the proposed framework are discussed and possible directions for future research are presented.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018DAP0021/_p
부
@ARTICLE{e102-d_4_800,
author={Wen-Lung TSAI, Yung-Chun CHAN, },
journal={IEICE TRANSACTIONS on Information},
title={Designing a Framework for Data Quality Validation of Meteorological Data System},
year={2019},
volume={E102-D},
number={4},
pages={800-809},
abstract={In the current era of data science, data quality has a significant and critical impact on business operations. This is no different for the meteorological data encountered in the field of meteorology. However, the conventional methods of meteorological data quality control mainly focus on error detection and null-value detection; that is, they only consider the results of the data output but ignore the quality problems that may also arise in the workflow. To rectify this issue, this paper proposes the Total Meteorological Data Quality (TMDQ) framework based on the Total Quality Management (TQM) perspective, especially considering the systematic nature of data warehousing and process focus needs. In practical applications, this paper uses the proposed framework as the basis for the development of a system to help meteorological observers improve and maintain the quality of meteorological data in a timely and efficient manner. To verify the feasibility of the proposed framework and demonstrate its capabilities and usage, it was implemented in the Tamsui Meteorological Observatory (TMO) in Taiwan. The four quality dimension indicators established through the proposed framework will help meteorological observers grasp the various characteristics of meteorological data from different aspects. The application and research limitations of the proposed framework are discussed and possible directions for future research are presented.},
keywords={},
doi={10.1587/transinf.2018DAP0021},
ISSN={1745-1361},
month={April},}
부
TY - JOUR
TI - Designing a Framework for Data Quality Validation of Meteorological Data System
T2 - IEICE TRANSACTIONS on Information
SP - 800
EP - 809
AU - Wen-Lung TSAI
AU - Yung-Chun CHAN
PY - 2019
DO - 10.1587/transinf.2018DAP0021
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2019
AB - In the current era of data science, data quality has a significant and critical impact on business operations. This is no different for the meteorological data encountered in the field of meteorology. However, the conventional methods of meteorological data quality control mainly focus on error detection and null-value detection; that is, they only consider the results of the data output but ignore the quality problems that may also arise in the workflow. To rectify this issue, this paper proposes the Total Meteorological Data Quality (TMDQ) framework based on the Total Quality Management (TQM) perspective, especially considering the systematic nature of data warehousing and process focus needs. In practical applications, this paper uses the proposed framework as the basis for the development of a system to help meteorological observers improve and maintain the quality of meteorological data in a timely and efficient manner. To verify the feasibility of the proposed framework and demonstrate its capabilities and usage, it was implemented in the Tamsui Meteorological Observatory (TMO) in Taiwan. The four quality dimension indicators established through the proposed framework will help meteorological observers grasp the various characteristics of meteorological data from different aspects. The application and research limitations of the proposed framework are discussed and possible directions for future research are presented.
ER -