A Redundancy-Based Optimization Approach for Aggregation in Multidimensional Scientific and Atatistical Databases

Large data volumes, flexible drill-down analysis and short query response times, which are predominant characteristics of Scientific and Statistical Data Base (SSDB) applications, require new optimization techniques as compared to traditional DBMS. This paper describes formally an optimization approach in the SSDB domain which is based on the re-use of materialized results offormer queries to process aggregate queries along a classi$cation hierarchy. The description of the approach is embedded in the CROSS-DB model (which stands for Classification-oriented, Redundancybased Optimization of Scientzj% and Statistical DataBases). It will be shown that the approach taken can improve query response time by orders of magnitude while adding tolerable storage and maintenance overhead to the database.