Parallel processing of multi-join expansion-aggregate data cube query in high performance database systems

Data-cube queries containing aggregate functions often combine multiple tables through join operations. We can extend this to "multi-join expansion-aggregate" data-cube queries by using more than one aggregate function in a "SELECT" statement in conjunction with relational operators. In parallel processing for such queries, it must be decided which attribute to use as a partitioning attribute, in particular the join attribute or "cube-by". Based on the partitioning attribute, we introduce three parallel multi-join expansion-aggregate data-cube query methods, namely the multi-join partition method (MPM), the expansion partition method (EPM) and the "early expansion partition with replication" method (EPRM). All three methods use the join attribute and "cube-by" as the partitioning attribute. A performance evaluation of the three parallel processing methods is also carried out and presented.