• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>East European conference on advances in databases and information systems >Improving the Pruning Ability of Dynamic Metric Access Methods with Local Additional Pivots and Anticipation of Information
【24h】

Improving the Pruning Ability of Dynamic Metric Access Methods with Local Additional Pivots and Anticipation of Information

机译：利用局部附加支点和信息预期提高动态度量访问方法的修剪能力

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Metric Access Methods (MAMs) have been proved to allow performing similarity queries over complex data more efficiently than other access methods. They can be considered dynamic or static depending on the pivot type used in their construction. Global pivots tend to compromise the dynamicity of MAMs, as eventual pivot-related updates must be propagated through the entire structure, while local pivots allow this maintenance to occur locally. Several applications handle online complex data and, consequently, demand efficient dynamic indexes to be successful. In this context, this work presents two techniques for improving the pruning ability of dynamic MAMs: (ⅰ) using cutting local additional pivots to reduce distance calculations and (ⅱ) anticipating information from child nodes to reduce unnecessary disk accesses. The experiments reveal significant improvements in a dynamic MAM, reducing execution time in more than 50 % for similarity queries posed on datasets ranging from moderate to high dimensionality and cardinality.
机译：事实证明，度量访问方法（MAM）可以比其他访问方法更有效地对复杂数据执行相似性查询。根据其构造中使用的枢轴类型，可以将它们视为动态的还是静态的。全局枢轴倾向于损害MAM的动态性，因为最终与枢轴相关的更新必须传播到整个结构中，而局部枢轴则允许这种维护在本地进行。多个应用程序处理在线复杂数据，因此，需要高效的动态索引才能成功。在这种情况下，这项工作提出了两种提高动态MAM修剪能力的技术：（ⅰ）使用局部局部枢轴切割来减少距离计算，以及（ⅱ）预期来自子节点的信息以减少不必要的磁盘访问。实验揭示了动态MAM的显着改进，对于从中等到高维度和基数的数据集提出的相似性查询，执行时间减少了50％以上。

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