Name
Abstract | ||
|---|---|---|
The cost of a query plan depends on many pa- rameters, such as predicate selectivities and available memory, whose values may not be known at optimization time. Parametric query optimization (PQO) optimizes a query into a number of candidate plans, each opti- mal for some region of the parameter space. We rst propose a solution for the PQO prob- lem for the case when the cost functions are linear in the given parameters. This solution is minimally intrusive in the sense that an ex- isting query optimizer can be used with minor modications: the solution invokes the con- ventional query optimizer multiple times, with dieren t parameter values. We then propose a solution for the PQO prob- lem for the case when the cost functions are piecewise-linear in the given parameters. The solution is based on modication of an exist- ing query optimizer. This solution is quite general, since arbitrary cost functions can be approximated to piecewise linear form. Both the solutions work for an arbitrary number of parameters. |
Year | Venue | Keywords |
|---|---|---|
2002 | VLDB | parametric query optimization,different parameter value,linear form,query optimizer multiple time,arbitrary number,pqo problem,piecewise linear cost function,cost function,existing query optimizer,query plan,arbitrary cost function,piecewise linear,parameter space,query optimization |
Field | DocType | Citations |
Query optimization,Mathematical optimization,Computer science,Parametric statistics,Parameter space,Predicate (grammar),Piecewise linear function,Query plan | Conference | 21 |
PageRank | References | Authors |
1.68 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arvind Hulgeri | 1 | 347 | 23.20 |
| S. Sudarshan | 2 | 2690 | 601.76 |