Compressed Data Cubes for OLAP Aggregate Query Approximation on Continuous Dimensions

Efficiently answering decision support queries is an important problem. Most of the work 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 the hierarchy along each dimension be fixed (hence dimensions are categorical or prediscretized). 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 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 dimen...

• Type: Conference
• Year: 1999
• Where: KDD
• Authors: Jayavel Shanmugasundaram, Usama M. Fayyad, Paul S. Bradley
• Keywords: Aggregation Functions, Data Cubes, Data Mining, KDD 1999, Target Measure