Efficient star query processing is crucial for a performant 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 time. 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. 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 of join operations significantly. With this method even queries covering a large part of the fact table can be executed within a time span acceptable for interactive query processing.

Online analytical processing (OLAP) requires query response times within the range of a few seconds in order to allow for interactive drilling, slicing, or dicing through an OLAP cube. While small OLAP applications use multidimensional database systems, large OLAP applications like the SAP BW rely on relational (ROLAP) databases for efficient data storage and retrieval. ROLAP databases use specialized data models like star or snowflake schemata for data storage and create a large set of indexes or materialized views in order to answer queries efficiently. In our case study, we show the performance benefits of TransBase HyperCube, a commercial RDBMS, whose kernel fully integrates the UB-Tree, a multi-dimensional extension of the B-Tree. With this newly developed access structure, TransBase HyperCube enables interactive OLAP without the need for storing a large set of materialized views or creating a large set of indexes. We compare not only the query performance, but also consider index size and maintenance costs. For the case study we use a 42 million record ROLAP database of GfK, the largest German market research company.

Data warehousing applications cope with enormous data sets in the range of Gigabytes and Terabytes. Queries usually either select a very small set of this data or perform aggregations on a fairly large data set. Materialized views storing pre-computed aggregates are used to efficiently process queries with aggregations. This approach increases resource requirements in disk space and slows down updates because of the view maintenance problem. Multidimensional hierarchical clustering (MHC) of OLAP data overcomes these problems while offering more flexibility for aggregation paths. Clustering is introduced as a way to speed up aggregation queries without additional storage cost for materialization. Performance and storage cost of our access method are investigated and compared to current query processing scenarios. In addition performance measurements on real world data for a typical star schema are presented.