Compressed Data Cubes for OLAP Aggregate Query Approximation on Continuous Dimensions

Efficiently answering decision support queries is an important problem. Most of the work done in this direction has been in the context of the data cube. Queries are efficiently answered by pre-computing large parts of the cube. Besides having large space requirements, such pre-computation requires that hierarchy along each dimension be fixed (hence dimensions are categorical or pre-discretized). Queries that take advantage of pre-computation can thus only drill-down or roll-up along this fixed hierarchy. Another disadvantage of existing pre-computation techniques is that the target measure, along with the aggregation function of interest, is fixed for each cube. Queries over more than one target measure or using different aggregate functions, along with the aggregation function of interest, is fixed for each cube. Queries over more than one target measure or using different aggregation functions, would require pre-computing larger data cubes. In this paper, we propose a new compressed representation of the data cube that (a) drastically reduces storage requirements, (b) does not require the discretization hierarchy along each query dimension to be fixed beforehand and (c) treats each dimension as a potential target measure and supports multiple aggregation functions without additional storage costs. The tradeoff is approximate, yet relatively accurate, answers to queries. We outline mechanisms to reduce the error in the approximation. Our performance evaluation indicates that our compression technique effectively addresses the limitations of existing approaches.