面向多属性条件的空间对象检索算法

Spatial Object Retrieval Algorithms for Multi-attribute Conditions

针对现有空间索引不能满足多样化的检索需求,提出两种新型空间索引,能够同时面向空间属性、文本属性与划分属性进行空间对象检索。将分类技术应用于空间对象检索中,提出了基于划分索引与IR-Tree的混合索引以及先划分索引再IR-Tree的索引方法,不仅满足了多样化的空间检索需求,而且有效地解决了传统空间索引更新维护代价大的问题。基于真实的北京市POI数据集进行实验,结果表明本文提出两种索引是有效的且检索效率高。与传统空间索引相比,提出的空间索引很好地解决了具有划分属性的空间对象检索问题,并且具有较高的检索效率。

In view of the fact that the existing spatial indexes cannot meet diversified retrieval requirements,this paper proposes two new types of spatial indexes,which can search the spatial object with spatial attributes,text attributes and dividing attributes simultaneously.We apply classification techniques to the existing spatial object retrieval,and propose two spatial indexes including a hybrid index based on both dividing index and IR-Tree and first dividing index then IR-Tree index.Both spatial indexes not only meet the requirement of diversified spatial retrieval,but also solve the problem of the traditional spatial index’s high maintenance cost effectively.The experiments based on the real data set show that the two indexes are effective and efficient.Compared with the traditional spatial indexes,the spatial indexes we proposed solve the problem of spatial object retrieval with dividing attribute,and achieve higher retrieval efficiency.