Query rewriting for semantic query optimization in spatial databases

Traditional database query optimisation methods use stochastic algorithms to approximate the query optimisation results by continuously adjusting the optimisation plan. Since the stochastic algorithm only performs query optimisation from a single perspective, it leads to no significant improvement of the optimised database query efficiency. To address the above problems, we studied the query optimisation method of foreign enterprises’ German language data database based on hybrid learning. By reducing the database query search space and selecting query optimisation strategy, the data query complexity is reduced. After estimating the cost of database query optimisation, the policy selection algorithm is trained using the hybrid learning theory to obtain the database query optimisation path. The simulation experimental results show that the average query response of the optimised database after applying the studied method saves about 13.6%, and the query cost is lower and the optimisation effect is better.

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Spatial Information Retrieval in Digital Ecosystems

Carniel

2020

Proceedings of the 12th International Conference on Management of Digital EcoSystems

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Query rewriting for semantic query optimization in spatial databases

Cited by 4 publications

References 14 publications

Defining and designing spatial queries: the role of spatial relationships

Defining and designing spatial queries: the role of spatial relationships

Optimisation of German Language Database Query for Foreign Companies Based on Hybrid Learning

Spatial Information Retrieval in Digital Ecosystems

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