III: Small: Aspects of Integrating Heterogeneous and Inconsistent Data

III：小：集成异构和不一致数据的方面

• 批准号: 1217869
• 负责人: Phokion Kolaitis
• 金额: $ 48.1万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217869&HistoricalAwards=false
• 关键词: III; Small; Aspects; Integrating; Heterogeneous

Integrating heterogeneous data is a major challenge that manifests itself across a wide spectrum, from government and business to science and health care. A critical task underlying this challenge is deriving the relationship between different database schemas. During the past decade, schema mappings have emerged as the right tool for this task. Schema mappings are high-level, declarative specifications of the relationships between two database schemas that provide the appropriate level of abstraction and, at the same time, can be compiled into executable code.The first main aim of this project is to develop a framework and tools for designing schema mappings. This framework is based on the systematic use of data examples. The project investigates fundamental algorithmic tasks in using data examples as a device to illustrate and understand the behavior of already derived schema mappings, and also as inputs to a schema-mapping design system that will derive a suitable schema mapping based on the given data examples. The second main aim is to investigate the uses of schema mappings in integrating and exchanging inconsistent data that arise when bringing together data from heterogeneous sources. Since the current framework of data exchange does not handle inconsistencies well, this project re-examines data exchange and extends it to gracefully handle inconsistencies.This project will advance the state of the art in designing schema mappings and managing inconsistent databases. Furthermore, it will contribute to the development of human resources in science and engineering through the teaching and research training of graduate students.

集成异构数据是一项重大挑战，从政府和企业到科学和医疗保健，这一挑战在很大范围内表现出来。这一挑战背后的一个关键任务是导出不同数据库模式之间的关系。在过去的十年中，模式映射已经成为完成这项任务的正确工具。模式映射是两个数据库模式之间关系的高级声明性规范，提供了适当的抽象级别，同时可以编译为可执行代码。本项目的第一个主要目标是开发一个用于设计模式映射的框架和工具。这一框架基于对数据实例的系统使用。该项目研究基本的算法任务，使用数据示例作为一种设备来说明和理解已经导出的模式映射的行为，并作为模式映射设计系统的输入，该系统将根据给定的数据示例导出合适的模式映射。第二个主要目的是调查使用模式映射集成和交换不一致的数据时，出现的数据从异构源汇集在一起。 由于目前的数据交换框架不能很好地处理不一致性，本项目重新研究数据交换，并扩展它以优雅地处理不一致性。本项目将推进设计模式映射和管理不一致数据库的最新技术。此外，它将通过研究生的教学和研究培训，为科学和工程领域的人力资源开发做出贡献。