Distributed query processing plan generation using iterative improvement and simulated annealing

Huge information is available in distributed database that can be exploited for constructive use. A query posed over a distributed database may get processed against disparate data sources distributed over a network. Each of these sources may contain data relevant to the query. The aim of distributed database system is to provide efficient query processing strategy for the given query. In distributed database scenario, multiple copies of the same data may reside at different sources. As a result, there can be multiple query strategy for a given query and finding an optimal query processing strategy is a combinatorial optimization problem. In this paper, an approach is presented that is able to generate optimal query processing plans for a given user query. The approach uses iterative improvement and simulated annealing algorithms to determine optimal query plans for a given query. The approach uses the cost heuristic defined in [1].