Data Warehouse Evolution: Consistent Metadata Management

Large information spaces such as the WWW face the problem of how to maintain data warehouses (views) de ned over Information Sources (ISs) whenever capabilities of these ISs change. We have developed a novel solution approach to address this problem, called the Evolvable View Environment (EVE). In EVE, knowledge of both the capabilities of as well as (partial) containment relationships between ISs is collected in a Meta Knowledge Base (MKB). The accuracy and consistency of the MKB has great importance to the EVE framework, because the MKB serves as an information pool enabling EVE to nd alternative replacements for components of views that have become affected by IS capability changes. In this paper, we describe the meta knowledge management problem and focus on issues related to the MKB evolution process. The contributions of this paper are threefold. In this paper, we rst formally de ne consistency criteria for the MKB evolution process. Second, we use PC constraint evolution to demonstrate our MKB evolution process. Third, we discuss techniques of keeping the MKB as powerful as possible by deriving and preserving minimal implicit knowledge from the a ected explicit meta knowledge, before retracting the latter. This work was supported in part by the NSF NYI grant #IRI 94-57609. Dr. Rundensteiner would also like to thank IBM for the IBM Partnership Award.