NSF-CNPq Collaborative Research on Integrating Geospatial Information

NSF-CNPq 地理空间信息整合合作研究

• 批准号: 9970123
• 负责人: Max Egenhofer
• 金额: $ 15万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-15 至 2004-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9970123&HistoricalAwards=false
• 关键词: NSF; CNPq; Collaborative; Research; Integrating

Under this project, researchers at the University of Maine and the Brazilian National Institute for Space Research (INPE) are collaborating to study new models for integrating geographic information. The two research teams complement each other, providing a unique synergy in conceptual modeling and systems development. Long-term visits by Brazilian researchers assure a tight cooperation, and provide for integrated research and education. The work focuses on the semantics of spatial data collections that are stored in geographic information systems or spatial databases. Such collections often have diverse database schemas and lack conventions that would make them easily compatible, so that they could be combined to perform an integrated analysis. The goal of this research is to develop computational models that will allow us to compare, harmonize, and integrate geographic information across different ontologies and different spatial data models. The approach consists of a new integrated method to assess similarity based on common parts, functions, and attributes. In these similarity assessments, different contexts are considered through the operations a user intents to perform with the harmonized data. The results of this project will provide meaningful comparisons and integration of geospatial information, enabling better interoperation among geographic information systems.http://www.spatial.maine.edu/~max/CNPq.html

在该项目下，缅因大学和巴西国家空间研究所 (INPE) 的研究人员正在合作研究整合地理信息的新模型。两个研究团队相辅相成，在概念建模和系统开发方面提供了独特的协同作用。巴西研究人员的长期访问确保了紧密的合作，并提供了综合研究和教育。这项工作的重点是存储在地理信息系统或空间数据库中的空间数据集合的语义。此类集合通常具有不同的数据库模式，并且缺乏使它们易于兼容的约定，因此可以将它们组合起来执行集成分析。这项研究的目标是开发计算模型，使我们能够比较、协调和集成不同本体和不同空间数据模型的地理信息。该方法包含一种新的集成方法，用于根据公共部件、功能和属性评估相似性。在这些相似性评估中，通过用户意图使用协调数据执行的操作来考虑不同的上下文。该项目的结果将提供地理空间信息的有意义的比较和整合，从而实现地理信息系统之间更好的互操作。http://www.spatial.maine.edu/~max/CNPq.html