自适应近似树

在大规模多媒体数据库中进行基于内容的检索，高维数据牵引结构的研究是重要问题，提出了一种有效的高维索引结构－自适应近似树，阐述了它的结构，给出了构建和检索算法，它结合了树结构和顺序检索的共同优点，针对不同的数据分布情况可以自适应地调整结构，维数较低或数据分布偏斜较大时它呈现树的结构，高维或数据分布密集时呈现顺序扫描的结构，以达到更优的检索效率，在结构上，对MBR使用了压缩存储的方法以节省存储空间，在算法中充分利用了空间划分是MBS和MBR共存的特点，减少了大量复杂的计算，从而大大提高检索效率。

ADAPTIVE APPROXIMATION TREE

The study of high dimensional data index method is the key problem of content based search in large scale multimedia databases. In this paper, an efficient high dimensional index structure called adaptive approximation tree (AA tree) is proposed. Its structure, the algorithm of its construction and searching are given in detail. The merits of both tree structures and sequential scan structures are effectively combined in AA tree so that it can adjust its structure adaptively according to data distribution to make search more efficient. Tree structure is used in low dimensionality or large data distribution skew, while it's of sequential scan structure when the dimensionality or data distribution density is high. In structure, a compressed method is used for MBR in order to save storage spaces. Because MBS and MBR are used simultaneously in AA tree's data space partition, a lot of complex calculations are decreased so that the search is accelerated obviously.