Khalil Amiri, Sanghyun Park, et al.
ICDE 2003
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.
Khalil Amiri, Sanghyun Park, et al.
ICDE 2003
Vijayshankar Raman, Amol Deshpande, et al.
ICDE 2003
Ioana Stanoi, George Mihaila, et al.
ICDE 2003
Rakesh Agrawal, Jerry Kiernan, et al.
ICDE 2003