Abstract: Many indices are designed to process near neighbors search as well as range query efficiently in high dimensional data space. It is proved that such indices can hardly do faster than linear scan for the sake of high dimensionality. This paper proposes a two level hierarchical index to support nearest neighbor search and range query simultaneously in high dimensional metric data space. A notable character of this index is that data clustering can be completed automatically in index constructing and this character results in its higher speed than linear scan on clustering datasets and almost the same speed as linear scan on other datasets. The top level is a binary tree consisting of well organized reference points. The bottom level is a series of dynamic hashing tables in which the dataset is hashed according to the distances from the data points to the reference points. The query processing procedure can be narrowed to scan a few buckets and use candidate answer to prune many buckets that are irrelevant to the query. Theoretical analysis and experiments show that our index structure performs well both in in-memory and secondary storage.

Key words: relative distance, high dimensional index, k-NN search

摘要： 人们设计了许多索引以有效地处理高维空间中的近邻查询和区域查询。已经证明，维数较高时利用高维索引处理这两类查询几乎不可能比线性扫描快。提出了一种两层索引以自适应地识别数据集中的聚簇；数据集具有聚簇特性时，用该索引处理邻近查询和区域查询比现有的索引结构快；对其他数据集，利用该索引处理邻近查询和区域查询与线性扫描大致相当。该索引的上层结构将一些参考点组织成一棵二叉树，下层结构是一系列动态哈希表。数据集中的数据点根据它们到参考点的相对距离被哈希到相应的哈希桶中。查询处理时用查询点到参考点的距离进行剪除搜索。实验表明，提出的索引结构具有良好的性能。

关键词: 相对距离, 高维索引, k-NN查询