海域动态监测数据治理框架研究
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摘要
基于海域动态监测业务需求,所采集的卫星遥感、航空遥感、远程监控、现场监测等海域动态监测数据是海域综合管理的重要依据。通过对数据的有效治理,实现数据价值最大化和海域综合服务支撑,是数据治理的主要目标。文章分析了海域动态监测数据的特点,阐明了数据治理的目标和意义。研究结果表明:海域动态监测数据具有多源多类性、海量性、时空性和多维性,为避免同类信息化平台重复建设和同一数据重复获取及处理,构建了海域动态监测数据治理框架,明确了数据治理的4个要素即:目标要素、促成要素、核心要素和支持要素,核心要素中的各项工作开展要以目标要素为导向,支撑要素是核心要素各项工作开展的基础,促成要素保障核心要素各项工作的实施。
The marine dynamic monitoring data of satellite remote sensing,aerial remote sensing,remote monitoring and field monitoring are the important basis for the comprehensive management of sea area,based on the demand of the sea area dynamic monitoring.Effective management of data,maximizing the value of data and supporting services in the sea area are the main objectives of data governance.This paper analyzed the characteristics of the marine dynamic monitoring data,and clarified the significance of data management.The results showed that the marine dynamic monitoring data were multisource,massive,multidimensional,temporality and spatiality.In order to avoid the repeated construction of similar information products and the repeated acquisition and processing of the same data,this paper built a data governance framework containing four elements.The elements are outcomes,enablers,core elements and supporting.The work of the core elements should be carried out with the outcomes as the guidance,and supporting elements are the basis for the work of the core elements,and the enablers are,the guarantee of the implementation of the work of the core elements.
The marine dynamic monitoring data of satellite remote sensing,aerial remote sensing,remote monitoring and field monitoring are the important basis for the comprehensive management of sea area,based on the demand of the sea area dynamic monitoring.Effective management of data,maximizing the value of data and supporting services in the sea area are the main objectives of data governance.This paper analyzed the characteristics of the marine dynamic monitoring data,and clarified the significance of data management.The results showed that the marine dynamic monitoring data were multisource,massive,multidimensional,temporality and spatiality.In order to avoid the repeated construction of similar information products and the repeated acquisition and processing of the same data,this paper built a data governance framework containing four elements.The elements are outcomes,enablers,core elements and supporting.The work of the core elements should be carried out with the outcomes as the guidance,and supporting elements are the basis for the work of the core elements,and the enablers are,the guarantee of the implementation of the work of the core elements.
引文
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[2]包冬梅,范颖捷,李鸣.高校图书馆数据治理及其框架[J].图书情报工作,2015,59(18):134-141.
[3]徐涛.浅谈银行业数据标准管理框架体系[J].金融经济,2017(18):129-130.
[4]姜树强,张建玲,李玲,等.综合医院健康体检大数据标准化构建与应用[J].空军医学杂志,2017,33(3):213-215.
[5]黄冬梅,赵丹枫,魏立斐,等.大数据背景下海洋数据管理的挑战与对策[J].计算机科学,2016,43(6):17-23.
[6]张一鸣.数据治理过程浅析[J].中国信息界,2012(9):15-17.
[7]DAMA International.The DAMA Guide to the Data Management Body of Knowledge[M].1st ed.New York:Technics Publications,2009:37.
[8]Data Governance Institute.The DGI Data Governance Framework[EB/OL].[2018-07-09].http://www.datagovernance.com/the-dgi-framework/.
[9]SOARES S.IBM数据治理统一流程[M].Ketchum:MC Press Online,LLC.,2010.
[10]张明英,潘蓉.《数据治理白皮书》国际标准研究报告要点解读[J].信息技术与标准化,2015(6):54-57.
[11]杨琳,高洪美,宋俊典,等.大数据环境下的数据治理框架研究及应用[J].计算机应用与软件,2017,34(4):65-69.
[12]司莉,曾粤亮.机构科研数据知识库联盟数据治理框架研究[J].图书馆论坛,2018(8):61-67.
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