R-树聚集在空间数据仓库中的应用

 空间数据库存储空间对象的相关信息,在很多实际应用中需要汇总空间数据,但这种汇总非常耗费时间而且计算代价很高.受非空间数据仓库的启发,可以建立空间数据仓库来加速空间OLAP操作.考虑星型模式且着重于空间维的概念分层,这种分层主要根据空间R-树索引来建立.提出了一个空间查询算法,并考虑了数据更新.     

An improved R-tree based on childnode''s probability

R-Tree is a good structure for spatial searching. But in this indexing structure，either the sequence of nodes in the same level or sequence of traveling these nodes when queries are made is random. Since the possibility that the object appears in different MBR which have the same parents node is different，if we make the subnode who has the most possibility be traveled first，the time cost will be decreased in most of the cases. In some case，the possibility of a point belong to a rectangle will shows direct proportion with the size of the rectangle. But this conclusion is based on an assumption that the objects are symmetrically distributing in the area and this assumption is not always coming into existence. Now we found a more direct parameter to scale the possibility and made a little change on te structure of R-tree，to increase te possibility of founding te satisfying answer in the front sub trees. We names this structure probability based arranged R-tree (PBAR-tree).     

一种新的空间数据库索引结构

提出一种新的空间数据索引结构．利用Hilbert曲线的聚类特性解决了R-tree查寻路径的非惟一性问题;通过最小外接矩形分解技术(DMBR)确定多边形的形状,直接获得空间对象的具体结构信息,获得了更精确的查寻结果;采用改进的分裂算法可提高节点分配的效率和节点的饱和度,减少I／O访问次数,缩短数据在内存与硬盘之间相互交换的时间,该算法已应用于地理信息系统实践中,取得了较好效果。     

空间数据库R-tree连接方法研究

空间连接是空间数据库中最重要最耗时的操作,国内外学者的研究多是基于索引结构空间连接的一次过滤方法,其中基于R-tree的空间连接算法被认为是一种高效的处理机制。本文提出了基于R-tree空间连接的二次过滤方法,缩小了过滤阶段的候选集大小,可有效提高查询效率。