The data classification query optimization method for the English online examination system based on the grid image analysis

Abstract

In the English network examination system, the big data distribution is highly coupled, the cost of data query is large, and the precision is not good. In order to improve the ability of the data classification and query in the English network examination system, a method of data classification and query in the English network examination system is proposed based on the grid region clustering and frequent itemset feature extraction of the association rules. Using the grid image analysis to improve the statistical analysis of the English performance analysis, the collection and storage structure analysis of the information resource data of the English network examination system is carried out, and the feature of the information flow of the English network examination system is extracted and the auto-correlation feature analysis of the running data of the English network examination system is carried out. The feature quantity of the frequent item sets of the association rules, which reflects the running state of the English network examination system is extracted. The feature quantity of the closed frequent items of the extracted association rules is identified and classified by using the distributed clustering method of the grid region. In order to improve the target orientation of the data repository query in the English network examination system, the classification query of the data repository in the English network examination system is realized. The simulation results show that this method shows high precision and real-time performance in the English network examination system.