基于Hamming范数的XML流相关性估测算法

Correlation Estimating Algorithm of XML Stream Based on Hamming Norms

在数据库理论中,如何在较小的空间条件下快速地比较不同的XML(extensible markup language)流的差异性是一个基本问题.在这一问题的研究中,人们提出了树编辑距离等测度来描述XML文本的差异性.提出了一种基于Hamming范数的l0测度——即XML树的不同子树的个数,并以此来刻画XML文本的相关性.在数据流模型下,给出了基于空间有界伪随机数发生器、稳态分布于哈希函数的l0测度的概率算法.理论上的时空复杂性分析、正确性证明与实验模拟结果表明,这一概率算法对问题的输入提供了一个理想的近似.

It is of great importance to compare the correlation of different XML （extensible markup language） streams in the limited space in the Database Theory. In the study of these problems, several measures are proposed, e.g. the tree-edit distance, to show the difference of XML trees. This paper proposes a natural measure lo employing Hamming norms, i.e. the number of distinct sub-trees between two XML trees, to estimate the correlation. Furthermore, a probabilistic estimating algorithm involving space-bounded pseudorandom generators, stable distributions and hash functions has been presented in the data stream model. Theoretical time/space complexity analysis, correctness proof and experimental simulation show that this algorithm can give a desired approximation.