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

该研究的目标是设计、分析和 对系统的关键部件进行可行性研究， 支持数据密集型和数字化的科学建模- 密集型应用，特别是在大规模 环境科学两大类问题， 调查是对复杂空间的明确支持- 计算建模中所需的时态对象 科学现象（以及它们的时空 关系）和透明的数据访问支持， 大型、异构和分布式数据源。作为产品， 在这项研究中，将开发以下工具： 定义和操纵复杂的时空 对象的数学和统计建模， 科学现象;支持模式的算法 更新和演变;空间和 时间操作.存储和操作规程 抽象;和合作科学研究的平台。 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， 它被选为典型的地球观测系统（EOS）， 项目然而，这项研究旨在使许多领域受益。 通过提供一套工具， 提高科学家的生产力， 使用大型数据集的复杂建模活动， 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.