Intelligent Prefetch Algorithm on Database Query Optimization
数据库查询优化中的智能预取技术

This paper explored a new approach toward intelligent caching and prefetching for data query of DBMS. First abstracted the data query statement into query patterns which consisted of four units. Also considered the real query parameters which could be used to build real query from the query pattern. Based on the query pattern and the real query parameters, it developed two intelligent prefetch algorithms to fit two kinds of demand in data query. The first algorithm based On-ant-group rule, It could be used to predict the future query with highest probability. Experiments showed that in contrast to the substantially large number of queriescoming of the special application to the database system, the number of patterns of these differentqueries were quite limited. It took into consideration the query pattern and the historytrace of query reference when predicting future query and developed the second algorithm based on inertia rule which used BP network to trace the history of user query. It was more fit for the multi-application situation than the previous. Simulation shows the inter-query locality is highly query pattern dependable under single-application situation and the inertia rule has more flexibility under multi-application situation.