Query Optimization Method Based on Simulated Annealing Algorithm

A simulated annealing algorithm and query optimization technology, which is applied in the field of relational database query optimization, can solve the problems of reducing search accuracy, prolonging search time, and local extremum cannot search in a wider range, so as to increase the search range and increase the probability , the effect of shortening the relative time of query optimization

Inactive Publication Date: 2017-02-15
JILIN UNIV
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Problems solved by technology

When the scale of the optimization problem is large and the search space becomes complex, most intelligent algorithms can search for a near-optimal solution, but due to the search mechanism of the algorithm, as well as the destruction of the components of the optimal solution by operations such as mutation and crossover in the later stage , will cause the algorithm to oscillate near the optimal solution, prolong the search time, or fall into a local extremum and cannot search in a wider range, reducing the search accuracy

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  • Query Optimization Method Based on Simulated Annealing Algorithm
  • Query Optimization Method Based on Simulated Annealing Algorithm
  • Query Optimization Method Based on Simulated Annealing Algorithm

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Embodiment

[0038] The database query optimization algorithm based on the simulated annealing algorithm, taking the query optimization of a query command that requires 8 steps to complete the query task as an example: includes the following steps:

[0039] a. Define a strategy space S, and assume that all query strategies in S need 8 steps to complete the query task;

[0040] b. Establish a mathematical model of query strategy cost evaluation based on graph structure, assuming that there are 9 nodes in the graph, from node i (i=1,2,...,9) to node j (j=1,2,... ,9) the path is d ij , the path represents the time spent from node i to node j, that is, the cost, and the cost matrix is What is sought is the total cost of traversing 9 nodes, and the cost function obtained by bringing n=9 into formula (1) is:

[0041]

[0042] Where: (x 1 ,x 2 ,...,x 9 ) is a solution X of the cost function f(X) 1 , where x i =(1,2,3...,9),x j =(1,2,3...,9),x i ≠ x j (i≠j), the set of all solutions ...

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Abstract

The invention relates to a query optimization method based on a simulated annealing algorithm. The method comprises the steps that a data query optimization process is divided into a model building part, a strategy space resolving part and an optimization part, then the simulated annealing algorithm is led in, all strategy space subsets are searched in a parallel mode, a final solution is obtained from each subset, and the optimal solution is obtained after the final solutions are compared. Compared with other intelligent optimization algorithms, the simulated annealing algorithm can effectively avoid a local extremum and shorten the optimization time. In addition, due to the utilization of parallel searching, the searching range of the simulated annealing algorithm is enlarged, and the influence on searching precision by local search characteristics of the simulated annealing algorithm can be reduced. Compared with the probability for searching an optimal strategy by a traditional local random searching algorithm, the probability for obtaining the optimal strategy by the query optimization method based on the simulated annealing algorithm is improved obviously. The query speed of a database is improved, the relative time of query optimization is shortened, and the probability for obtaining the optimal strategy is improved.

Description

technical field [0001] The invention relates to an optimization method for querying a relational database, in particular to an optimization method for improving the multi-link querying speed of a large-scale relational database. Background technique [0002] Relational database is a mainstream database based on mathematical concepts. It can directly describe the actual relationship and has high access efficiency. However, the data structure is relatively complicated, especially for large-scale relational databases. With the expansion of the application environment, its stored There are more and more types of data, and the quantity is also increasing, and the data structure becomes extremely complex. When performing multi-link query, the query efficiency is low. In order to enable the large-scale relational database system to respond to user operations in a timely manner and quickly provide query results, query optimization is often performed. Query optimization refers to se...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
Inventor 姜弢宋健徐学纯贾海青
Owner JILIN UNIV
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