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OLAP (online analytical processing) servers usually pre-compute data cubes to improve the response time of possible aggregate queries over cuboids with different grouping attributes. To reduce the huge size of a sparse data cube, the base single tuples (BSTs) are explored to condense cube tuples aggregated from the same set of source tuples into one tuple, whenever such condensing will not require further aggregate when the cube is used to answer queries. We propose the CuboidTree to index the BST condensed cube. Using both synthetic and real world data, we conducted experiments to demonstrate query processing and bulk incremental updating performance of the indexing scheme.