9312003 Silberschatz The use of data at different levels of information content is essential to the performance of very large databases because lower information content (or lower resolution) data is smaller and thus can significantly decrease I/O and communication costs. The ability to trade query response resolution for faster performance has been suggested as an approach to meeting real-time performance constraints. These two performance advantages of a multiresolution system can be exploited fully only by a database management system (DBMS) that supports the convenient retrieval of data at different levels of resolution. A data model that defines and supports the notions of multiresolution and incremental improvement of query responses, or "progressive refinement," is constructed. The basis of this model is a new construct, called the "sandbag," for representing imprecise information about a set. Practical, efficient algorithms that implement the sandbag, and thus enable a multiresolution DBMS, are developed. An extended query language that specifies the tradeoff of resolution for performance, and a mechanism for implementing such queries, are also developed. This research will provide solutions which effectively balance performance and cost for the information-processing challenges of next generation database applications such as multimedia, scientific, geographic, and other very large databases. ***
