As simulation is gaining popularity as an inexpensive means of experimentation in diverse fields of industry and government, the attention to the data generated by scientific simulation is also increasing. Scientific simulation generates mesh data, i.e. data configured in a grid structure, in a sequence of time steps. Its model is complex - understanding it involves mathematical topology and geometry in addition to fields (in the relational sense). Moreover, there is no query language developed on mesh data at all. We develop a comprehensive model of mesh data in an object-oriented manner, propose a set of primitive algebraic operators, show their object-oriented implementation and demonstrate that the well-known object query language OQL (from the ODMG) is powerful enough to express queries on mesh data, whether the queries are on a mesh topology, geometry, fields, or a combination of them. Finally, we discuss some physical implementation issues that are pertinent to executing queries efficiently.

Data warehouses and data marts have been successfully applied to a multitude of commercial business applications. They have proven to be invaluable tools by integrating information from distributed, heterogeneous sources and summarizing this data for use throughout the enterprise. Although the need for information dissemination is as vital in science as in business, working warehouses in this community are scarce because traditional warehousing techniques do not transfer to scientific environments. There are two primary reasons for this difficulty. First, schema integration is more difficult for scientific databases than for business sources, because of the complexity of the concepts and the associated relationships. While this difference has not yet been fully explored, it is an important consideration when determining how to integrate autonomous sources. Second, scientific data sources have highly dynamic data representations (schemata). When a data source participating in a warehouse changes its schema, both the mediator transferring data to the warehouse and the warehouse itself need to be updated to reflect these modifications. The cost of repeatedly performing these updates in a traditional warehouse, as is required in a dynamic environment, is prohibitive. This paper discusses these issues within the context of the DataFoundry project, an ongoing research effort at Lawrence Livermore National Laboratory. DataFoundry utilizes a unique integration strategy to identify corresponding instances while maintaining differences between data from different sources, and a novel architecture and an extensive meta-data infrastructure, which reduce the cost of maintaining a warehouse.

Mediators are a critical component of any data warehouse; they transform data from source formats to the warehouse representation while resolving semantic and syntactic conflicts. The close relationship between mediators and databases requires a mediator to be updated whenever an associated schema is modified. Failure to quickly perform these updates significantly reduces the reliability of the warehouse because queries do not have access to the most current data. This may result in incorrect or misleading responses, and reduce user confidence in the warehouse. Unfortunately, this maintenance may be a significant undertaking if a warehouse integrates several dynamic data sources. This paper describes a meta-data framework, and associated software, designed to automate a significant portion of the mediator generation task and thereby reduce the effort involved in adapting to schema changes. By allowing the DBA to concentrate on identifying the modifications at a high level, instead of reprogramming the mediator, turnaround time is reduced and warehouse reliability is improved.