An efficient method for maintaining data cubes incrementally

Abstract

The data cube operator computes group-bys for all possible combinations of a set of dimension attributes. Since computing a data Cube typically incurs a considerable cost, the data Cube is often precomputed and stored as materialized views in data warehouses. A materialized data cube needs to be updated when the source relations are changed. The incremental maintenance of a data cube is to compute and propagate only its changes, rather than recompute the entire data Cube from scratch. For n dimension attributes, the data cube consists of 2(n) group-bys. each of which is called a cuboid To incrementally maintain a data cube with 2(n) cuboids, the conventional methods Compute 2(n) delta cuboids, each of which represents the change of a cuboid In this paper. we propose an efficient incremental maintenance method that can maintain a data cube using only a subset of 2(n) delta cuboids We formulate an optimization problem to find the optimal subset of 2(n) delta cuboids that minimizes the total maintenance cost, and propose a heuristic solution that allows LIS to maintain a data cube using only (n inverted right perpendicularn/2inverted left perpendicular)delta cuboids. As a result, the cost of maintaining a data cube is substantially reduced Through various experiments, we show the performance advantages of the propo