A data warehouse is a repository storing integrated information for efficient querying and analysis. Information is extracted from heterogeneous sources as it is generated or updated. The information is then translated into a common data model and integrated with exisiting data at the warehouse. When a user query is submitted to the warehouse, the needed information is already there, with inconsistencies and differences already resolved. This makes it much easier and more efficient to run queries over data that originally came from different sources. Key advantages of data warehousing include:

• Since query execution does not involve data translation and communication with remote sources, complex queries can be executed easily and efficiently.
• End users can use a single data model and query language.
• System design becomes simpler. For example, there is no need to perform query optimization over heterogeneous sources, a very difficult problem faced by other approaches.
• Information sources may be unreliable and may purge data. On the other hand, information at the warehouse is under the control of the warehouse users; it can be stored safely and reliably for as long as necessary.

The goal of WHIPS (WareHouse Information Prototype at Stanford) is to develop algorithms and tools for the creation and maintenance of data warehouses. The WHIPS project is funded by Rome Laboratories and by the Advanced Research and Development Committee of the Community Management Staff as part of the Massive Digital Data Systems (MDDS) Program.