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.
