Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more
Pruning attribute values from data cubes with diamond dicing
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
On power-law distributed balls in bins and its applications to view size estimation
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
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On-Line Analytical Processing (OLAP) aims at gaining useful information quickly from large amounts of data residing in a data warehouse. To improve the quickness of response to queries, pre-aggregation is a useful strategy. However, it is usually impossible to pre-aggregate along all combinations of the dimensions. The multi-dimensional aspects of the data lead to combinatorial explosion in the number and potential storage size of the aggregates. We must selectively pre-aggregate. Cost/benefit analysis involves estimating the storage requirements of the aggregates in question. We present an original algorithm for estimating the number of rows in an aggregate based on the Pareto distribution model. We test the Pareto Model Algorithm empirically against four published algorithms, and conclude the Pareto Model Algorithm is consistently the best of these algorithms for estimating view size.