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

摘要

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 of former queries to process aggregate queries along a classification hierarchy. The description of the approach is embedded in the CROSS-DB model (which stands for Classification-oriented, Redundancy-based Optimization of Scientific and Statistical Data-Bases). It will be shown that the approach taken can improve query response time by orders of magnitude while adding tolerable storage and maintenance over-head to the database.