图结构数据的高效结构索引

Yingjie Fan, Chenghong Zhang, Shuyun Wang, Xiulan Hao, Yunfa Hu
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引用次数: 0

摘要

为了加快对XML和半结构化数据的查询,提出了许多结构化索引。结构索引通常是一个带标签的有向图,通过将XML数据图中的节点划分为等价类并将等价类存储为索引节点来定义。在相关连续树(IRST)的基础上,提出了一种有效的自适应结构指数——IRST(k)-指数。实验结果表明,与之前的A(k)'-索引、D(k)-索引和M(k)-索引相比,IRST(k)-索引在空间消耗和查询性能方面表现得更加高效,同时使用的构建时间也显著减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Efficient Structural Index for Graph-Structured Data
To speed up queries over XML and semi-structured data, a number of structural indexes have been proposed. The structural index is usually a labeled directed graph defined by partitioning nodes in the XML data graph into equivalence classes and storing equivalence classes as index nodes. On the basis of the Inter- Relevant Successive Trees (IRST), we propose an efficient adaptive structural index, IRST(k)-index. Compared with the previous indexes, such as the A(k)'-index, D(k)- index, and M(k)-index, our experiment results show that the IRST(k)-index performs more efficiently in terms of space consumption and query performance, while using significantly less construction time.
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