Toward a System that Supports Conceptual Modeling in Data Intensive Scientific Investigation

该研究的目标是设计、分析和执行系统关键组件的可行性研究，这些组件支持数据密集型和数字密集型应用中的科学建模，特别是在大规模环境科学领域。正在研究的两组主要问题是明确支持科学现象（以及它们的时间和空间关系）的计算建模所需的复杂时空对象，以及透明地支持从大型、异构和分布式数据源访问数据。作为这项研究的成果，将开发下列工具：用于定义和操纵复杂的时空对象的语言以及用于科学现象的数学和统计建模的语言；支持模式更新和进化的算法；空间和时间操作的优化技术；存储和操作抽象协议；以及合作科研的平台。科学应用的特定领域涉及数据库支持调查亚马逊流域的水文，这被选为典型的地球观测系统（EOS）项目。然而，这项研究旨在通过提供一套工具来提高科学家的生产力，从而使自然科学的许多领域受益，这些科学家需要将复杂的建模活动与大型数据集的使用相结合，这是大多数EOS项目中遇到的问题。

The goal of the proposed research is to design, analyze, and perform feasibility studies of key components of systems that support scientific modeling in data-intensive and numerically- intensive applications, particularly in the area of large-scale environmental science. The two major sets of issues under investigation are explicit support for the complex spatio- temporal objects that are required in the computational modeling of scientific phenomena (as well as their temporal and spatial relationships) and transparent support for data access from large, heterogeneous and distributed data sources. As products of this research, the following tools will be developed: languages for the definition and manipulation of complex, spatio-temporal objects and for the mathematical and statistical modeling of scientific phenomena; algorithms for the support of schema updates and evolution; optimization techniques for spatial and temporal manipulations; protocols for storage and operation abstraction; and platforms for collaborative scientific research. The particular domain of scientific application involves database support for investigating the hydrology of the Amazon basin, which is chosen as a typical Earth Observation System (EOS) project. The research, however, is designed to benefit many areas of the natural sciences by providing a set of tools for increasing the productivity of scientists who need to integrate complex modeling activities with the use of large datasets, which is a problem encountered in most EOS projects.