Computing Iceberg Cubes by Top-Down and Bottom-Up Integration: The StarCubing Approach

Data cube computation is one of the most essential but expensive operations in data warehousing. Previous studies have developed two major approaches, top-down versus bottom-up. The former, represented by the multiway array cube (called the multiway) algorithm, aggregates simultaneously on multiple dimensions; however, it cannot take advantage of a priori pruning when computing iceberg cubes (cubes that contain only aggregate cells whose measure values satisfy a threshold, called the iceberg condition). The latter, represented by BUC, computes the iceberg cube bottom-up and facilitates a priori pruning. BUC explores fast sorting and partitioning techniques; however, it does not fully explore multidimensional simultaneous aggregation. In this paper, we present a new method, star-cubing, that integrates the strengths of the previous two algorithms and performs aggregations on multiple dimensions simultaneously. It utilizes a star-tree structure, extends the simultaneous aggregation methods, and enables the pruning of the group-bys that do not satisfy the iceberg condition. Our performance study shows that star-cubing is highly efficient and outperforms the previous methods