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Dimension table driven approach to referential partition relational data warehouses

Most of business intelligence applications use data warehousing solutions. The star schema or its variants modelling these applications are usually composed of hundreds of dimension tables and multiple huge fact tables. Referential horizontal partitioning is one of physical design techniques adapted to optimize queries posed over these schemes. In referential partitioning, a fact table can inherit the fragmentation characteristics from dimension table(s). Most of the existing works done on referential partitioning start from a bag containing selection predicates defined on dimension tables, partition each one based on its predicates and finally propagate their fragmentation schemes to the fact table. This procedure gives all dimension tables the same probability to partition the fact table which is not always true. In order to ensure a high performance of the most costly queries, the identification of relevant dimension table(s) to referential partition a fact table is a crucial issue that should be addressed. In this paper, we first study the complexity of the problem of selecting dimension table(s) used to partition a fact table. Secondly, we present strategies to perform their selection. Finally, to validate of our proposal, we conduct intensive experimental studies using a mathematical cost model and the obtained results are verified on Oracle11G DBMS.