데이타 웨어하우스에서 데이타 큐브를 위한 효율적인 점진적 관리 기법

초록

The data cube is an aggregation operator that computes group-bys for all possible combination of dimension attributes. When the number of the dimension attributes is n, a data cube computes 2n group-bys. Each group-by in a data cube is called a cuboid. Data cubes are often precomputed and stored as materialized views in data warehouses. These data cubes need to be updated when source relation change. The incremental maintenance of a data cube is to compute and propagate only its changes. To compute the change of a data cube of 2n cuboids, previous works compute a delta cube that has the same number of cuboids as the original data cube. Thus, as the number of dimension attributes increases, the cost of computing a delta cube increases significantly. Each cuboid in a delta cube is called a delta cuboid. In this paper, we propose an incremental cube maintenance method that can maintain a data cube by using only nCn/2 delta cuboids. As a result, the cost of computing a delta cube is substantially reduced. Through various experiments, we show the performance advantages of our method over previous methods.