Abstract
In existing database systems there is a strong need for searching data according to many attributes. In commercial database platforms, the standard search over multiple attributes is provided by B$^{+}$-tree (or it's variants) with compound keys.
On the other hand, such systems provide also multidimensional indexing, however, just for spatial purposes (such as GIS or CAD applications) and use special data types and querying syntax. In this paper we propose a native multidimensional method for indexing tables with simple attributes, such that multi-attribute queries can be processed (with standard SQL queries) more efficiently than by simple B$^{+}$-tree with compound keys.
For implementation we have used the PostgreSQL and R-tree-based index, though our method is applicable to any other multidimensional indexing method. With this combination we outperformed commercial platforms (Oracle, SQL Server) by an order of magnitude in the number of accesses to index.
As a by-product, a framework for easy implementation of external indexing methods into PostgreSQL was designed.