时空数据集的连接处理和优化方法研究

由于空间连接运算是空间数据库中最复杂、最耗时的基本操作,因此其处理效率在很大程度上决定了空间数据库的整体性能。目前空间连接算法基本上都是针对静态数据集的,而对于有动态数据集参与的空间连接问题研究还比较少。主要研究了静态数据集和动态数据集的空间连接问题,用R-tree和TPR-tree结构分别索引静态数据集和动态数据集。在连接中通过已经查找过的点,调整动态树MBR使筛选范围缩小,提高连接效率。     

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 the structure of R tree, to increase the possibility of founding the satisfying answer in the front sub trees. We names this structure probability based arranged R tree (PBAR tree).     

An Index mechanism for multi-scale view in spatial databases

There are numerous geometric objects stored in the spatial databases. An importance function in a spatial database is that users can browse the geometric objects as a map efficiently. Thus the spatial database should display the geometric objects users concern about swiftly onto the display window. This process includes two operations:retrieve data from database and then draw them onto screen. Accordingly, to improve the efficiency, we should try to reduce time of both retrieving object and displaying them. The former can be achieved with the aid of spatial index such as R-tree, the latter require to simplify the objects. Simplification means that objects are shown with sufficient but not with unnecessary detail which depend on the scale of browse. So the major problem is how to retrieve data at different detail level efficiently. This paper introduces the implementation of a multi-scale index in the spatial database SISP (Spatial Information Shared Platform) which is generalized from R-tree. The difference between the generalization and the R-tree lies on two facets: One is that every node and geometric object in the generalization is assigned with a importance value which denote the importance of them, and every vertex in the objects are assigned with a importance value,too. The importance value can be use to decide which data should be retrieve from disk in a query. The other difference is that geometric objects in the generalization are divided into one or more sub-blocks, and vertexes are total ordered by their importance value. With the help of the generalized R-tree, one can easily retrieve data at different detail levels.Some experiments are performed on real-life data to evaluate the performance of solutions that separately use normal spatial index and multi-scale spatial index. The results show that the solution using multi-scale index in SISP is satisfying.     

基于K-Means聚类的R-树空间索引方法研究与分析

 空间聚类和空间索引的结合是当前空间数据库中提高数据检索效率的技术之一。本文从空间聚类和空间索引的存储原理入手,阐述了K-Means聚类算法及其改进算法的技术思路,研究了K-Means算法在空间数据库中与空间索引方法结合的技术问题;分析了当前基于K-Means算法的R-树系列空间索引技术的研究成果,阐述了它们提高空间检索效率的技术路线及实验结果,研究显示这些技术都能在一定程度上提高数据检索的效率。最后给出了聚类与空间索引结合技术未来的研究方向。     

Processing Constrained K Closest Pairs Query in Spatial Databases

In this paper, constrained K closest pairs query is introduced, wbich retrieves the K closest pairs satisfying the given spatial constraint from two datasets. For data sets indexed by R trees in spatial databases, three algorithms are presented for answering this kind of query. Among of them, two-phase Range＋Join and Join＋Range algorithms adopt the strategy that changes the execution order of range and closest pairs queries, and constrained heap-based algorithm utilizes extended distance functions to prune search space and minimize the pruning distance. Experimental results show that constrained heap-base algorithm has better applicability and performance than two-phase algorithms.     

基于混合聚类算法的动态R-树

针对动态R-树是通过动态插入算法建立起来的, 其节点分裂算法的性能直接影响到R-树的性能和查询效率的问题, 为了使动态R-树适应多维复杂空间数据的要求, 提出一种用于实现R-树节点分裂的混合聚类算法（HCR）, 它建立在普通聚类算法的基础上, 并进行了一系列扩充. 针对空间对象的均匀分布与不均匀分布, HCR算法在实现R-树节点分裂时分别采用不同的聚类准则以提高其聚类效果和查询效率. 此外, 还将HCR算法与其他算法进行对比实验, 结果表明该算法具有较高的查询效率.     

面向分布式存储的空间数据放置方法研究

目前,互联网中海量空间数据采用分布式存储,空间数据放置直接关系到数据访问效率.为了提高分布式存储中空间数据访问效率,提出了一种DHT-R数据放置策略,该策略将分布式哈希表（DHT）和R树相结合,按照分布式哈希表存储空间数据基本信息和索引地址,同时以R树型结构组织和存放空间数据,R树存储使得快速访问空间数据成为可能.实验发现,数据存取的可靠性较高,数据的吞吐时延也明显低于业界的阀值,DHT-R放置方法在大量访问压力下依然能良好的平衡和并发.     

基于位置的可拼接轨迹对搜索

移动设备的快速发展,生成了大量轨迹.基于位置的轨迹搜索,是指给定一组查询点,从数据集中检索top-k条轨迹,但是所得到的轨迹可能不能近距离通过所有查询点.利用轨迹可拼接的想法,提出基于位置的可拼接轨迹对搜索,使用户利用轨迹对得到的轨迹更加近距离地通过所有查询点.在搜索终止过程,给出可拼接的轨迹对搜索过程的有效终止条件.真实的数据集验证了所提方法的有效性.      

Parallel bulk-loading of spatial data with MapReduce: An R-tree case

Current literature on parallel bulk-loading of R-tree index has the disadvantage that the quality of produced spatial index decrease considerably as the parallelism increases.To solve this problem,a novel method of bulk-loading spatial data using the popular MapReduce framework is proposed.MapReduce combines Hilbert curve and random sampling method to parallel partition and sort spatial data,thus it balances the number of spatial data in each partition.Then the bottom-up method is introduced to simplify and accelerate the sub-index construction in each partition.Three area metrics are used to test the quality of generated index under different partitions.The extensive experiments show that the generated R-trees have the similar quality with the generated R-tree using sequential bulk-loading method,while the execution time is reduced considerably by exploiting parallelism.     

An Index mechanism for multi-scale view in spatial databases

There are numerous geometric objects stored in the spatial databases. An importance function in a spatial database is that users can browse the geometric objects as a map efficiently. Thus the spatial database should display te geometric objects users concern about swiftly onto the display window. This process includes two operations：retrieve data from database and then draw them onto screen. Accordingly,to improve the efficiency,we should try to reduce time of both retrieving object and displaying them. The former can be achieved with the aid of spatial index such as R-tree,the latter require to simplify the objects. Simplification means that objects are shown with sufficient but not with unnecessary detail which depend on the scale of browse. So the major problem is how to retrieve data at different detail level efficiently. This paper intrrduces the implementation of a multi-scale index in the spatial database SISP (Spatial Information Shared Platform ) which is generalized from R-tree. The difference between the generalization and the R-tree lies on two facets ： One is that every node and geometric object in the generalization is assigne+ with a importance value which +enote the importance of them,and every vertex in the objects are assigned with a importance value,too. The importance value can be use to decide which data should be retrieve from disk in a query. The other difference is that geometric objects in the generalization are divided into one or more sub-blocks,and vertexes are total ortiered by their importance value. With the help of the generalized R-tree,one can easily rtrieve data at differnt detail levels. Some experiments are performed on real-life data to evaluate the performance of solutions that separately use normal spatial index and multi-scale spatial index. The results show that the solution using multi-scale index in SISP is satisfying.