计算机科学 ›› 2010, Vol. 37 ›› Issue (1): 158-162.

• 软件工程与数据库技术 • 上一篇    下一篇

Rav-tree:一种有效支持反向近似近邻查询的索引结构

李博涵,郝忠孝   

  1. (哈尔滨理工大学计算机科学与技术学院 哈尔滨150080);(哈尔滨工业大学计算机科学与技术学院 哈尔滨150001)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(60673136),黑龙江省自然科学基金项目(F200601)资助。

Rav-tree:An Efficient Index Structure for Reverse Approximate Nearest Neighbor Query

LI Bo-han,HAO Zhong-xiao   

  • Online:2018-12-01 Published:2018-12-01

摘要: 空间数据库的索引结构是实现有效数据查询的前提和基础。空间数据反向近似近邻查询是空间查询的一个新方向,它避免了精确查询中过多的距离计算,从而能够在效率与准确性上取得平衡。提出的Rav-tree不同于基于启发式规则的索引结构,首先利用局部近似,然后根据Voronoi cell区域和估计圆的方法实现近似近部查询,并利用过滤结果和分域查询得到初步的候选集,最终通过反向近似近部查询(RANKQuery)算法得到RANK集,并完整地给出基于Rav-tree的ANN查询算法和RANN查询算法。实验结果表明,Rav-tree对RANN等查询具有较好的查询效率和查全率。

关键词: 索引结构,反向近似近邻,分域查询,区域估计

Abstract: Index structure is the precondition and foundation in the efficient data query. The reverse approximate nearest neighbor query is a new issue in the area of spatial query. This approach can avoid much metric distance computation in exact query, and acquire a better tradeoff between the efficiency and precision. The Rav-tree is different from the index structures based on the heuristic rules. It applies partial Voronoi cell approximation with estimated circles to filter the results of approximate nearest neighbor query. The final result set of RANK is reached through the algorithms of ANN query and Division ctuery with primary candidates. The experimental results indicate that the Rav-tree is an effective index structure and has better efficiency and recall for the query such as RANN query.

Key words: Index structure, Reverse approximate nearest neighbor, Division cauery, Region estimation

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