ITR/IM+AP: Developing the National Virtual Observatory (NVO) Data Model

本项目的目标是探索开发一种数据模型，该模型将定义抽象的规则集，以允许国家虚拟天文台(NVO)对大型、多样化的数据集进行操作。该项目是美国建造NVO的更大天文学努力的一部分。美国国家科学院天文学十年调查(2001年)强烈支持NVO项目，并建议美国国家科学基金会和美国宇航局为其提供资金。该项目是NVO的重要的第一步。在NVO的“访问引擎”上开始广泛开发之前，必须先建立一个定义明确的数据模型的基本框架，并且在广泛开发最终的、由科学驱动的分析工具之前，必须先建立好访问引擎。然而，由于数据模型、访问引擎和分析工具的开发不能作为一个连续的过程成功完成，因此必须在迭代过程的每个阶段对NVO的每个方面进行测试和重新开发，将分析工具、访问引擎和数据模型一起测试，然后重新设计以适应最终的科学驱动因素。参与这一特定项目的科学家和计算机专家可以访问当地社区的各种不同数据(从X射线到无线电)，并计划将这些不同数据用作实验数据模型的试验场。该项目作为初步开发测试得到资助，为期两年，以确保它将保持根据未来发展挑战重新设计数据模型所需的灵活性。***

AST-0121296GoodmanThe goals of this project are to explore the development of a data model that will define the abstract set of rules to permit the National Virtual Observatory (NVO) to operate on large, diverse data sets. The project is part of the larger US astronomy effort to build an NVO. The National Academy of Sciences Decadal Survey of Astronomy (2001) strongly endorsed the NVO project and recommended that both NSF and NASA fund it. This project is one of the important first steps in the NVO. The rudiments of a well-defined data model must be in place before extensive development can begin on the "access engine" for the NVO, and the access engine must be in place before extensive development of the final, science-driven, analysis tools can be carried out. Since, however, the development of a data model, an access engine, and analysis tools can not be done successfully as a sequential process, there must be testing and redevelopment of each aspect of the NVO at every stage in an iterative process with analysis tools, access engine, and data model tested together and then re-engineered to fit the final science drivers. The scientists and computer specialists involved in this particular project have access to a wide-range of different data (from X-ray to radio) in their local community and plan to use these different data as a testing ground for an experimental data model. This project has been funded as an initial development test, for two years, to assure that it will maintain the flexibility needed to re-engineer the data model in the light of future developmental challenges. ***