Abstract:
A data cube is a popular organization for summary data. A cube is simply a multidimensional structure that contains at each point an aggregate value, i.e., the result of applying an aggregate function to an underlying relation. In practical situations, cubes can require a large amount of storage. The typical approach to reducing storage cost is to materialize parts of the cube on demand. Unfortunately, this lazy evaluation can be a time-consuming operation. In this paper, we propose an approximation technique that reduces the storage cost of the cube without incurring the run time cost of lazy evaluation. The idea is to characterize regions of the cube by using statistical models whose description take less space than the data itself. Then, the model parameters can be used to estimate the cube cells with a certain level of accuracy. To increase the accuracy, some of the "outliers," i.e., cells that incur in the largest errors when estimated can be retained. The storage taken by the mod...
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