{"title":"Using Dublin Core to Build a Common Data Architecture","authors":"Sandra Fricker Hostetter","doi":"10.1400/39301","DOIUrl":null,"url":null,"abstract":"The corporate world is drowning in disparate data. Data elements, field names, column names, row names, labels, metatags, etc. seem to reproduce at whim. Librarians have been battling data disparity for over a century with tools like controlled vocabularies and classification schemes. Data Administrators have been waging their own war using data dictionaries and naming conventions. Both camps have had limited success. A common data architecture bridges the gap between the worlds of tabular (structured) and non-tabular (unstructured) data to provide a total solution and clear understanding of all data. Using the Dublin Core Metadata Element Set Version 1.1 and its Information Resource concept as building blocks, the Rohm and Haas Company Knowledge Center has created a common data architecture for use in the implementation of an electronic document management system (EDMS). This platform independent framework, when fully implemented, will provide the ability to create specific subsets of enterprise data on demand, enable interoperability with other internal or external systems, and reduce cycle time when migrating to the next generation tool.","PeriodicalId":122537,"journal":{"name":"Dublin Core Conference","volume":"160 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Dublin Core Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1400/39301","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The corporate world is drowning in disparate data. Data elements, field names, column names, row names, labels, metatags, etc. seem to reproduce at whim. Librarians have been battling data disparity for over a century with tools like controlled vocabularies and classification schemes. Data Administrators have been waging their own war using data dictionaries and naming conventions. Both camps have had limited success. A common data architecture bridges the gap between the worlds of tabular (structured) and non-tabular (unstructured) data to provide a total solution and clear understanding of all data. Using the Dublin Core Metadata Element Set Version 1.1 and its Information Resource concept as building blocks, the Rohm and Haas Company Knowledge Center has created a common data architecture for use in the implementation of an electronic document management system (EDMS). This platform independent framework, when fully implemented, will provide the ability to create specific subsets of enterprise data on demand, enable interoperability with other internal or external systems, and reduce cycle time when migrating to the next generation tool.
企业界正淹没在不同的数据中。数据元素、字段名、列名、行名、标签、元标签等似乎可以随意复制。一个多世纪以来,图书馆员一直在用受控词汇表和分类方案等工具与数据差异作斗争。数据管理员一直在使用数据字典和命名约定进行他们自己的战争。两个阵营都取得了有限的成功。一个通用的数据体系结构弥合了表格(结构化)和非表格(非结构化)数据之间的鸿沟,从而提供了一个完整的解决方案和对所有数据的清晰理解。Rohm and Haas公司知识中心使用都柏林核心元数据元素集1.1版及其信息资源概念作为构建块,创建了一个用于实现电子文档管理系统(EDMS)的通用数据体系结构。这个平台独立的框架在完全实现后,将提供按需创建特定的企业数据子集的能力,支持与其他内部或外部系统的互操作性,并减少迁移到下一代工具时的周期时间。