DCA-Tree: a high performance structure for incremental update cube on MDDW

Abstract

MOLAP (multidimensional online analytical processing) is an important application on multidimensional data warehouse. We often execute range queries on aggregate cube computed by pre-aggregate technique in MOLAP. In this paper, we propose an high performance and incremental update cube which can reduce the update cost significantly while maintaining reasonable search efficiency, by using an index structure called the data cube aggregate tree (DCA-Tree) which is an improved version of the R*-Tree. The DCA-Tree stores the aggregate values for the minimum bounding rectangle (MBR) of the data cube by some pre-aggregate technology and thus to minimize the update cost since only a small fraction of data nodes in the data cube is changed. When we insert the new dimension, the DCA-Tree locates the address of the insertion node and inserts the new data nodes of the new insert dimension into the double linked list of the data nodes by modify the pointers of those data nodes. Then incrementally update the affected the ancestor's of the insertion data nodes from the data nodes to the root node.