SoD: Data and Meta-Data Integration Maintenance

SoD：数据和元数据集成维护

• 批准号: 0438866
• 负责人: Lise Getoor
• 金额: --
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0438866&HistoricalAwards=false
• 关键词: SoD; Data; Meta; Integration; Maintenance

This research project focuses on developing a data integration and transformation process that supports maintainability, adaptability, and evolution. Data integration systems are software systems that permit the transformation, integration, and exchange of structured data that has been designed and developed independently. The often subtle and complex interdependencies within data can make the creation, maintenance, and use of such systems quite challenging. The PI, with the collaborator Renee Miller (University of Toronto) have available a robust arsenal of tools and mechanisms for reconciling semantic differences in how data is represented including views, mappings, and transformation languages. The major focus is on the maintenance of the metadata necessary to achieve semantic integration and sharing of data. This project develops an integration and transformation process that is designed for evolution. The research will develop a new theory of metadata discovery and adaptation based on modern statistical learning and a new theory of the data integration process, that supports not only automation, but also maintainability, adaptability and evolution. The major contribution of this research will be the development of a design process that supports robust data sharing, an crucial aspect in the Science of Design. As part of the broader impacts of this work, the expected results will contribute to an enhanced infrastructure for research by developing a benchmark for schema and mapping discovery and management tasks. The research results and the benchmark that will be accessible on the project Web site (http:/www.cs.umd.edu/~getoor/sod) to facilitate dissemination of knowledge and tools to a variety of scientific communities. The methods developed in this project will be of particular value to scientists who routinely need to gather, manage, and integrate diverse data sets. The researchers also plan to partner with industry collaborators in order to learn from and address industry needs, receive feedback, and facilitate technology transfer.

该研究项目的重点是开发支持可维护性、适应性和演进的数据集成和转换过程。数据集成系统是独立设计和开发的允许转换、集成和交换结构化数据的软件系统。数据中往往微妙而复杂的相互依赖性使得此类系统的创建、维护和使用变得相当具有挑战性。该 PI 与合作者 Renee Miller（多伦多大学）提供了强大的工具和机制库，用于协调数据表示方式（包括视图、映射和转换语言）方面的语义差异。主要重点是维护实现语义集成和数据共享所需的元数据。该项目开发了一个专为进化而设计的集成和转换过程。该研究将开发基于现代统计学习的元数据发现和适应的新理论以及数据集成过程的新理论，该理论不仅支持自动化，而且支持可维护性、适应性和演化。这项研究的主要贡献将是开发支持强大数据共享的设计流程，这是设计科学的一个重要方面。作为这项工作更广泛影响的一部分，预期结果将通过开发模式基准以及映射发现和管理任务来增强研究基础设施。研究结果和基准可在项目网站（http://www.cs.umd.edu/~getoor/sod）上获取，以促进向各种科学界传播知识和工具。该项目开发的方法对于经常需要收集、管理和整合不同数据集的科学家来说特别有价值。研究人员还计划与行业合作者合作，以学习和解决行业需求、接收反馈并促进技术转让。