Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions

As a result of decades of research and industrial development, modern query optimizers are complex software artifacts. However, the quality of the query plan chosen by an optimizer is largely determined by the quality of the underlying statistical summaries. Small selectivity estimation errors, propagated exponentially, can lead to severely sub-optimal plans. Modern optimizers typically maintain one-dimensional statistical summaries and make the attribute value independence and join uniformity assumptions for efficiently estimating selectivities. Therefore, selectivity estimation errors in today's optimizers are frequently caused by missed correlations between attributes. The authors present a selectivity estimation approach that does not make the independence assumptions.

Provided by: VLD Digital Topic: Data Management Date Added: Sep 2011 Format: PDF

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