...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Chinese Journal of Electronics >Selectivity Estimation for String Predicates Based on Modified Pruned Count-Suffix Tree
【24h】

Selectivity Estimation for String Predicates Based on Modified Pruned Count-Suffix Tree

机译：基于改进的修剪后缀树的字符串谓词选择性估计

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The accuracy of predicates selectivity estimation is one of the important factors affecting query optimization performance. State-of-art selectivity estimation algorithms for string predicates based on Pruned countsuffix tree (PST) often suffer severe underestimating and overestimating problems, thus their average relative errors are not good. We analyze the main causes of the underestimating and overestimating problems, propose a novel Restricted pruned count-suffix tree (RPST) and a new pruning strategy. Based on these, we present the EKVI algorithm and the EMO algorithm which are extended from the KVI algorithm and the MO algorithm respectively. The experiments compare the EKVI algorithm and the EMO algorithm with the traditional KVI algorithm and the MO algorithm, and the results show that the average relative errors of our selectivity estimation algorithms are significantly better than the traditional selectivity estimation algorithms. The EMO algorithm is the best among these algorithms from the overall view.
机译：谓词选择性估计的准确性是影响查询优化性能的重要因素之一。基于修剪后缀树（PST）的字符串谓词的最新选择性估计算法经常遭受严重的低估和高估问题，因此它们的平均相对误差并不理想。我们分析了低估和高估问题的主要原因，提出了一种新颖的限制修剪后缀树（RPST）和一种新的修剪策略。在此基础上，提出了分别从KVI算法和MO算法扩展而来的EKVI算法和EMO算法。实验比较了EKVI算法和EMO算法与传统的KVI算法和MO算法，结果表明，我们的选择性估计算法的平均相对误差明显优于传统的选择性估计算法。从总体上看，EMO算法是这些算法中最好的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号