Parallelizing the Data Cube

This paper presents a general methodology for the efficient parallelization of existing data cubeconstruction algorithms.We describe two different partitioning strategies, one for top-down and one for bottomup cube algorithms. Both partitioning strategies assign subcubes to individual processors in suchaway that the loads assigned to the processors are balanced. Our methods reduce inter-processor communication overhead by partitioning the load in advance instead of computing each individual group-by in parallel as is done in previous parallel approaches