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Issue No.01 - January (2007 vol.19)
pp: 111-126
Jiawei Han , IEEE
Dong Xin , IEEE
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 [30], aggregates simultaneously on multiple dimensions; however, it cannot take advantage of a priori pruning [2] 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 [6] , 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.
Data warehouse, data mining, online analytical processing (OLAP).
Jiawei Han, Xiaolei Li, Zheng Shao, Dong Xin, "Computing Iceberg Cubes by Top-Down and Bottom-Up Integration: The StarCubing Approach", IEEE Transactions on Knowledge & Data Engineering, vol.19, no. 1, pp. 111-126, January 2007, doi:10.1109/TKDE.2007.4
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