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

Yan Chengcheng ()
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Yan Chengcheng: College of Languages and Culture Communications, Xi’an Mingde Institute of Technology, Shaanxi, Xi’an 710124, P. R. China

Journal of Information & Knowledge Management (JIKM), 2022, vol. 21, issue Supp02, 1-15

Abstract: 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.

Keywords: Hybrid learning; German language materials for foreign companies; database query; optimisation methods; fish swarm algorithm (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0219649222400196

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