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 functions in "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, join attribute or cube-by. Based on the partitioning attribute, we introduce three parallel multi-join expansion-aggregate data cube query methods, namely Multi-join Partition Method (MPM), Expansion Partition Method (EPM) and Early Expansion Partition with Replication Method (EPRM). All three methods use the join attribute and cube-by as the partitioning attribute. Performance evaluation of the three parallel processing methods is also carried out and presented here.