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

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2 Author(s)
Taniar, D. ; Sch. of Bus. Syst., Monash Univ., Clayton Campus, Vic., Australia ; Boon-Noi Tan, R.

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

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Parallel Architectures, Algorithms and Networks, 2002. I-SPAN '02. Proceedings. International Symposium on

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