| 摘要 |
Database system and methods are described for improving execution speed of database queries (e.g., for decision support). A multi-attribute selectivity optimization methodology is described that provides a more accurate estimate of the cost of a query execution plan, so that the predicted performance of the final execution plan will be more accurate. The densities by how much the selectivity deviates from a single attribute density and by how much the multi-attribute densities differ from one another are used as a basis for multi-selectivity estimates. The multi-attribute densities are used to scale estimates between extremes of total independence and total dependence. By taking into account how well attributes are correlated, the approach is able to provide more accurate multi-selectivity estimates. As a result, the database system can formulate better query plans and, thus, provide better performance.
|
