Processing relational OLAP queries with UB-Trees and multidimensional hierarchical clustering

Multidimensional access methods like the UBTree can be used to accelerate almost any query processing operation, if proper query processing algorithms are used: Relational queries or SQL queries consist of restrictions, projections, ordering, grouping and aggregation, and join operations. In the presence of multidimensional restrictions or sorting, multidimensional range query or Tetris algorithms efficiently process these operations. In addition, these algorithms also efficiently support queries that generate some hierarchical restrictions (for instance by following 1:n foreign key relationships). In this paper we investigate the impacts on query processing in RDBMS when using UB-Trees and multidimensional hierarchical clustering for physical data organization. We illustrate the benefits by performance measurements of queries for a star schema from a real world application of a SAP business information warehouse. The performance results reported in this paper were measured with our prototype implementation of UB-Trees on top of Oracle 8. We compare the performance of UB-Trees to native query processing techniques of Oracle, namely access via an index organized table, which essentially stores a relation in a clustered B*-Tree, and access via a full table scan of an entire relation. In addition we measure the performance of the intersection of multiple bitmap indexes to answer multidimensional range queries.