Efficient star query processing is crucial for a performance data warehouse (DW) implementation and much work is available on physical optimization (e.g., indexing and schema design) and logical optimization (e.g., pre-aggregated materialized views with query rewriting). One important step in the query processing phase is, however, still a bottleneck: the residual join of results from the fact table with the dimension tables in combination with grouping and aggregation. This phase typically consumes between 50% and 80% of the overall processing times. In typical DW scenarios pre-grouping methods only have a limited effect as the grouping is usually specified on the hierarchy levels of the dimension tables and not on the fact table itself. In this paper, we suggest a combination of hierarchical clustering and pre-grouping as we have implemented in the relational DBMS Transbase. Exploiting hierarchy semantics for the pre-grouping of fact table result tuples is several times faster than conventional query processing. The reason for this is that hierarchical pre-grouping reduces the number the join operations significantly. With this method even queries covering a large part of the table can be executed within a time span acceptable for interactive query processing.
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