Dynamic Optimization Analysis of Keyword Query Results in Relational Databases Based on Ant Colony Optimization Algorithm

When using Keyword relational database retrieval technology, users does not need any SQL language and the underlying database schema knowledge. For example, users simply use search engines like Web to obtain the relevant data in the database. KSORD has become a research focus in the field of database, however, the key technology is accurate query, and it cannot implement fuzzy query well. In this paper, the fuzzy range query based on digital attributes is developed after deeply studying SEEKER system, and the sorting strategy of the result set. In the SEEKER system, the scoring function used to sort the results and the correlation factors are not standardized, greatly affecting the sorting accuracy. Therefore, the ant colony optimization algorithm is standardized to handle relevant factors. Query and statistical analysis show that the method of standardized treatment, is better than the traditional method of improvement. So after the proposed membership function and the fuzzy operator method are defined, the fuzzy query based on key words can be implemented and analyzed with examples.