分布式空间数据分片与跨边界拓扑连接优化方法

研究分布式空间数据库(distributed spatial database,简称DSDB)中数据按区域分片时的跨边界片段拓扑连接查询问题,并提出相应的优化方法.首先研究了分布式环境下的空间数据的分片与分布,提出了空间数据分片的扩展原则:空间聚集性、空间对象的不分割性、逻辑无缝保持性.然后,将区域分割分片环境下的片段连接分为跨边界和非跨边界两类;同时,将拓扑关系分为两类,重点研究跨边界的两类片段拓扑连接.提出了跨边界空间片段拓扑连接优化的两个定理,并给出了证明.以此为基础,给出了跨边界空间拓扑连接优化规则,包括连接去除规则和连接优化转化规则.最后设计了详细的实验,对自然连接策略、半连接策略以及所提出的连接策略进行效率比较,结果表明,所提出的方法对跨边界连接优化有明显优势.因此,所提出的理论和方法可以用于分布式跨边界拓扑关系查询的优化.

Distributed Spatial Data Fragmentation and Cross-Border Topological Join Optimization

This paper aims at explaining the cross-border topological join queries of spatial fragments of the zone fragmentation data in distributed spatial database (DSDB), and the optimizing methods for such queries are proposed. First, the fragmentation and distribution of spatial data in a distributed environment are discussed, and the extra principles for spatial data fragmentation are put forward, including spatial clustering, non-partitioning on spatial objects, and maintaining logical seamless. Then, the fragment joins in zone fragmentation are classified into two categories: cross-border join and non-cross-border join; the topological relationships are also classified into two categories. Thus, the emphasis is put on the two types of cross-border topological joins. Two theorems for cross-border topological join optimization are proposed and proved. Based on the theorems, the optimizing rules for cross-border spatial topological join are given, including the removing rules and the transforming rules of fragment joins. Finally, tests are designed to compare three join strategies that include Na?ve join strategy, semi-join strategy and the proposed strategies. The results show that the proposed methods greatly improve the cross-border join optimizing efficiency. Therefore, the theorems and methods proposed in this work can be applied to the optimization of distributed cross-border spatial topological queries.