关键词: 联机分析处理（OLAP）, 缓存, 联机分析处理（OLAP）缓存, 朴素贝叶斯算法, 缓存机制, 数据仓库

Abstract: In the era of the big data, cache can be seen as one of the most effective ways to enhance data processing technique, and therefore it is widely researched. The majority of cache mechanism saves the query results as the file, thus there is nearly no way to reuse the partial data in the cache under specific situations, and consequently cache resources are wasted. Based on learning the cache techniques both here and abroad, this project designs one data warehouse cache mechanism by using incremental learning naive Bayesian algorithm. This cache mechanism can decide whether to cache the current query results according to users’ recent operations, and ultimately can increase the hit rate of cache. Finally, the results of the experiment illustrate the effectiveness and efficiency of this cache mechanism by analyzing both average query time and the hit rate of cache.

Key words: On-Line Analytical Processing（OLAP）, cache, On-Line Analytical Processing（OLAP） cache, naive Bayesian algorithm, caching mechanism, data warehouse