Access Optimization of Teaching Resource Management Database Based on Semantic Association Mining

Access Optimization of Teaching Resource Management Database Based on Semantic Association Mining

연구 분야: Databases

학회: 2021 International Conference on Education, Information Management and Service Science (EIMSS)

In order to improve the service quality of teaching resource management database, it is necessary to construct an optimized access scheduling model of personalized intelligent teaching resource management database, and propose an access scheduling algorithm of personalized intelligent teaching resource management database based on semantic association mining. Using fuzzy fusion degree feature parameter set matching and adaptive filtering method, the block combination control model of personalized data in teaching resource management database is obtained. Constructing a semantic combination autocorrelation distribution detection model of personalized data of teaching resource management database, adopting rough set feature matching and joint cross-correlation feature detection methods to carry out personalized data fusion, cluster analysis and feature reorganization of teaching resource management database, carrying out personalized access and information clustering of teaching resource management database in the reorganized feature space, combining resource combination allocation and joint mining methods. A fuzzy clustering model for adaptive access and scheduling of teaching resource management database is established. According to the results of fuzzy dynamic detection and fusion clustering, the feature matching in the process of access and scheduling of personalized intelligent teaching resource management database is realized. The adaptive access and scheduling model of teaching resource management database is established by the combination of cluster block clustering and piecewise fusion clustering. Tests show that this method has high accuracy and good matching for adaptive access and scheduling of teaching resource management database.