According to the present invention, a method of optimization by simulated annealing is provided that uses a spatial metric to localize the simulated annealing temperature, the move set, and the objects which the moves operate on. The method keeps a local history of the optimization process. The localization allows the simulated annealing process to adaptively control the annealing schedule of each local region independently. This allows the annealing temperature, move set, and the objects upon which the move set operates to each be adjusted for each region independently to maximize efficiency. This results in optimization of all regions in a quick and efficient manner.

Localized simulated annealing

The query processing in a mobile computing environment involves join processing among different sites which include static servers and mobile computers. Because of the presence of asymmetric features in a mobile computing environment, the conventional query processing for a distributed database cannot be directly applied to a mobile computing system. In this paper, we first explore some unique features of a mobile environment and then, in light of these features, devise query processing methods for both join and query processing. Remote mobile joins are said to be effectual if they are, when being interleaved into a join sequence, able to reduce the amount of data transmission cost required for distributed mobile query processing. Since mobile relations are employed as reducers in our proposed query processing cost model, more mobile joins in the query processing lead to less data transmitted through the network. With proper scheduling, interleaving effectual remote mobile joins into a query scheduling can significantly reduce the total amount of data transmission among different sites. A simulator is developed to evaluate the performance of algorithms devised. Our results show that the approach of interleaving the processing of distributed mobile queries with effectual remote mobile joins is not only efficient, but also effective in reducing the total amount of data transmission cost required to process distributed mobile queries.

Processing distributed mobile queries with interleaved remote mobile joins

Distributed relational database query optimisation is a combinatorial optimisation problem. This paper reports on an initial investigation into the potential for a genetic algorithm (GA) to optimise distributed queries. A genetic algorithm is developed and its performance compared with alternative stochastic optimisation techniques: random search, multistart and simulated annealing. The problem of fully reducing all tables in a tree query is used to compare the techniques. For this problem, evaluating the fitness function is an expensive operation. The proposed GA uses a tree-structured data model with tailored crossover and mutation operators that avoid the need to fully re-evaluate the fitness function for new solutions. Query optimisation is a task that must be performed in real-time. A technique is required that performs well at the start of a search, but avoids the problem of premature convergence. The proposed GA uses a local search phase to deliver the required real-time performance. Experiments show that the proposed GA can perform better than the alternative techniques tested. The potential for a GA to deliver valuable distributed query processing cost reductions is demonstrated.

Genetic algorithm optimisation of distributed database queries

A method of estimating the query size of two databases T and R is disclosed. The method uses a threshold value to categorize the databases as dense or sparse. A dense-dense procedure is then applied to the two databases to produce a dense-dense estimate (Ad). A sparse-any procedure that suppresses the dense data items coming from database T is performed which produces a first sparse-any estimate (As1). A second sparse-any estimate (As2) is then produced by suppressing the dense data items from database R . Ultimately a query size estimate is produced by combining the dense-dense estimate, the first sparse-any estimate and the second sparse-any estimate.

A method for skew resistant join size estimation

Simple and fast novel diversification approach for the UBQP based on sequential improvement local search

In relational distributed databases a query cost consists of a local cost and a transmission cost. Query optimization is a combinatorial optimization problem. As the query size grows, the optimization methods based on exhaustive search become too expensive. We propose the following strategy for solving large distributed query optimization problems in relational database systems: (1) represent each query-processing schedule by a labeled directed graph; (2) reduce the number of different schedules by pruning away invalid or high-cost solutions; and (3) find a suboptimal schedule by combinatorial optimization. We investigate several combinatorial optimization techniques: random search, single start, multistart, simulated annealing, and a combination of random search and local simulated annealing. The utility of combinatorial optimization is demonstrated in the problem of finding the (sub)optimal semijoin schedule that fully reduces all relations of a tree query. The combination of random search and local simulated annealing was superior to other tested methods.

Q.M. Malluhi

Papers

Combinatorial optimization of distributed queries