A Rapid Dimension Hierarchical Aggregation Algorithm on High Dimensional Olap
Kongfa Hu, Ling Chen, Haidong Liu, Jiajia Liu, Changhai Zhang · 2006
In the high dimensional DW, we full materialized the data cube impossibly. In this paper, we propose a novel aggregation algorithm, DHEPA, to vertically partition a high dimensional dataset into a set of disjoint low dimensional datasets called fragment mini-cubes. Using inverted hierarchical encoding indices and pre-aggregated results, OLAP queries are computed online by dynamically constructing cuboids from the fragment mini-cubes. As a result, the method we proposed in this paper can greatly reduce the disk I/Os and highly improve the efficiency of OLAP queries