基于双树双索引结构的移动查询方法

为实现对有限范围内海量移动对象的有效索引,构建通用的移动查询解决方案,针对移动对象在道路网格中的运动特点,提出了预测实时运动速度的速度积累模型和预测未来聚集位置的基于双树双索引结构的移动对象查询方法.双树双索引结构利用网格划分思想构建空间分割树,实现对现有GG TPR-tree查询结构的拓展,并结合GG TPR-tree索引及建立于内存中的Hash索引以满足各种类型的移动查询请求.仿真实验表明,在回答受限范围内海量移动对象的确定性查询和统计性查询时,与传统方法相比,双树双索引结构在查询结果准确率方面有明显的改善.     

基于三维网格-R树的混合索引方法研究

针对三维场景下空间数据分布不均匀呈现区域密集的问题,本文提出并建立了三维网格-R树混合索引结构,在此基础上给出详细的维护与查询算法。该混合索引结构综合了网格快速划分三维空间以及R树高效查询的优点,较好地解决了海量非均匀分布的三维数据的快速管理、查询问题。最后针对上述混合索引结构模型构建了实验系统,对不同大小、不同分布下的数据集进行范围查询、k近邻查询对比测试,实验结果均表明了该混合索引结构在查询方面的良好性能。     

移动对象数据库中的索引机制

无线通信技术和定位技术以其显著的实用性和先进性成为近年来的热门研究课题 ,同时各种应用中对移动对象的定位和跟踪能力的要求也越来越高。在 R*树的基础上提出一种多维空间索引结构 TPR树 ,以实现对活动在 (或可能活动在 )某区域内的移动对象的快速查询。分析和解决了 TPR树在查询、插入、删除和适时更新等处理中存在的问题。最后通过综合实验测试对所提方案进行性能评价     

移动对象数据库中的索引机制

无线通信技术和定位技术以其显著的实用性和先进性成为近年来的热门研究课题，同时各种应用 中对移动对象的定位和跟踪能力的要求也越来越高。在R*树的基础上提出一种多维空间索引结构TPR 树，以实现对活动在(或可能活动在)某区域内的移动对象的快速查询。分析和解决了TPR树在查询、插入、 删除和适时更新等处理中存在的问题。最后通过综合实验测试对所提方案进行性能评价。     

基于改进R*树的移动GIS多尺度渐进传输与表示

针对传统单一尺度空间数据在低速无线网络环境中难以由移动GIS下载与表示的问题,在研究矢量数据多尺度表示与R*树空间索引的基础上,设计并实现了改进的多尺度R*树空间索引算法.该算法使得R*树中非叶子结点能够关联合适尺度的空间对象实体,保证海量的矢量数据在服务器端得到了有效的多尺度组织.在该算法的基础上,借助于移动GIS端所设计的缓冲式多尺度空间数据存储与管理,以及基于请求/应答模型的GML流式数据传输,设计并实现了面向移动GIS的矢量数据多尺度渐进传输模型,从而使得移动GIS能够以较高的效率下载和表示各种尺度的空间数据.通过真实的土地利用现状空间数据实验,验证了该方法在无线网络情况下能够有效地提高移动GIS的数据传输效率.     

基于混合索引树的空间文本匹配算法

针对空间文本对象流和订阅流的匹配,采用一种混合索引树来组织数据对象,包括多叉树空间索引、谓词索引和倒排文件三个部分,其中多叉树空间索引用于空间区域管理,谓词索引和倒排文件用于订阅谓词管理.在此基础上,提出了谓词索引建立算法、空间文本对象与倒排项匹配算法和混合索引树检索算法.与基于空间网格加倒排文件的检索方法进行了对比实验,结果表明:所提出的算法提高了用户的检索效率,并验证了其有效性.     

移动对象流量查询方法研究

针对移动对象数据库中存储有移动对象运动状态信息的特点,提出了在移动对象数据库中实现基于道路网络的移动对象流量查询。首先,给出了利用直方图处理基于时间段的移动对象流量数据的方法;其次,基于FNR-Tree思想,提出了一个新的索引结构IFNR-Tree(Improved FNR-Tree),该索引结构增加了hash表和网格结构能够对移动对象数目进行统计,从而可以实现移动对象的流量查询。     

QGrid:一种空间移动对象并行索引结构

为提高空间移动对象数据更新效率和查询准确率,提出了一种空间移动对象并行索引结构.利用主索引和辅助索引支持对空间对象进行基于范围的查询和基于对象标识的查询,还通过查询索引将更新操作和可能受其影响的查询操作相连接,在满足并行操作时间片语义的同时,避免了传统方法进行范围查询时对查询范围内相关对象及相关索引结构全部进行锁定的需求.实验结果表明:高负载环境下,该索引结构不但能保证查询准确率,其处理能力也明显优于传统索引结构.该索引通过提高系统并行度,使同一范围内的更新和查询操作可以并行执行,提升了系统整体运行效率.     

适合于配电网GIS系统的空间索引研究

探讨了一种空间数据的组织方式,以及在这种方式下为了加快检索速度而建立的空间索引的方法.针对配电网络的实际应用情况,分析对比了BSP树、KDB树、R 树、网格划分等方法针对二维空间数据索引的效率,及其优缺点.详细讨论了适合于配电网络的网格索引机制的建立方法.     

路网中基于RQOP树的移动对象索引技术研究

基于位置的信息服务需要高效的索引方法来管理移动对象.针对PMR QUAD树索引路网空间时不平衡、部分路段重复存储且索引结构可调整性差的问题,用RQOP树对路网空间按照路段的空间分布进行划分,使树的高度尽可能低,改进基于路网的动态组合索引结构.对照实验表明,基于RQOP树的索引结构提高了查询效率.     

An index method for continuous moving objects

There are current, historical and future information about continuously moving spatio-temporal objects. And there are correspondingly spatio-temporal indexes for current, past and future querying. Among the various types of spatio-temporal access methods, no one can support historical and future information querying. The Time Parameterized R-tree(TPR-tree) employs the idea of parametric bounding rectangles in the R-tree. It can effectively support predictive querying to continuously moving objects. Unfortunately, TPR-tree can not used to historical querying. This paper presents a partial-persistence method in order to extend TPR-tree for querying past information of moving objects. In this method, several TPR-trees will be created for more effectively predictive querying, because TPR-tree has a time horizon limit for predictive querying. Further more, a B-tree will be used to index time dimension. Since the partial-persistence method brings about huge storage space using, this paper also discusses some methods on how to reduce storage space. Finally, this paper presents an extensive experimental study for the proposed method and gives some interesting directions for future work.     

An index method for continuous moving objects

There are current, historical and future information about continuously moving spatio-temporal objects. And there are correspondingly spatio-temporal indexes for current, past and future querying. Among the various types of spatio-temporal access methods, no one can support historical and future information querying. The Time Parameterized R-tree(TPR-tree) employs the idea of parametric bounding rectangles in the R-tree. It can effectively support predictive querying to continuously moving objects.Unfortunately, TPR-tree can not used to historical querying. This paper presents a partial-persistence method in order to extend TPR-tree for querying past information of moving objects. In this method, several TPR-trees will be created for more effectively predictive querying, because TPR-tree has a time horizon limit for predictive querying.Further more, a B-tree will be used to index time dimension. Since the partial-persistence method brings about huge storage space using, this paper also discusses some methods on how to reduce storage space. Finally, this paper presents an extensive experimental study for the proposed method and gives some interesting directions for future work.     

Indexing large moving objects from past to future with PCFI

In moving object database, the moving objects' current position must be kept in memory, also to the trajectory, in some case, as same as the future. But the current existing indexes such as SEB-tree, SETI-tree, 2+3R-tree, 2-3RT-tree and etc. can only provide the capability for past and current query, and the TPR-Tree, TPR*-Tree and etc.can only provide the capability for current and future query. None of them can provide a strategy for indexing the past, current and also the future information of moving objects.In this paper, we propose the past-current-future Index (PCFI-Index) to index the past,current & future information of the moving objects. It is the combination of SETI-tree and TPR*-tree, the SETI liking index is used for indexing the historical trajectory segments except the front line structure, and the moving objects' current positions, velocities are indexed via the in-memory frontline structure which mainly implemented with TPR*-tree.Considering the large update operations on TPR-tree of large population, a hash table considering cache sensitivity is also introduced. It works with the frontline part, leading a bottom-up update of the tree. The performance analysis proves that the PCFI-index can handle most of the query efficiently and provides a uniform solution for the trajectory query, time-slice query, internal query and moving query.     

面向移动对象范围的混合索引结构

许多时空应用(如火灾模拟等)需要高效地查询移动对象的变化范围,针对此需求提出了基于TPR-tree和GF索引方法的两种混合索引结构,以支持对移动对象当前和未来范围的预测时空查询.在代价模型分析的基础上,基于模拟数据集的实验结果表明,这种混合索引方法能够有效地支持对移动对象变化范围的预测查询.     

一种改进的基于交通网络的移动对象索引方法

通过对基于交通网络(简称网络)移动对象索引方法FNR-Tree的分析，提出了一种改进的TNR-Tree方法。该方法充分利用网络信息，增大空间索引粒度，使用更合理的时间间隔，加强对轨迹的索引。性能分析说明了TNR-Tree方法较大程度地减少数据存储量和索引尺寸，提高了插入性能，并能有效地进行轨迹索引。     

一种改进的基于交通网络的移动对象索引方法

通过对基于交通网络（简称网络）移动对象索引方法FNRTree的分析,提出了一种改进的TNRTree方法。该方法充分利用网络信息,增大空间索引粒度,使用更合理的时间间隔,加强对轨迹的索引。性能分析说明了TNRTree方法较大程度地减少数据存储量和索引尺寸,提高了插入性能,并能有效地进行轨迹索引。     

基于固定网络的移动对象轨迹查询

在基于固定网络的移动对象轨迹查询方面.现有的索引模型只能管理移动对象当前和过去或将来轨迹的查询,它们都不能同时实现移动对象的现在,过去和将来轨迹查询.本文在IMORS的索引结构基础上进行改进并提出了一种新的索引结构.它能实现基于固定网络的移动对象的全时态索引.     

Indexing large moving objects from past to future with PCFI

In moving object database, the moving objects' current position must be kept in memory, also to the trajectory, in some case, as same as the future. But the current existing indexes such as SEB tree, SETI tree, 2+3R tree, 2 3RT tree and etc. can only provide the capability for past and current query, and the TPR Tree, TPR * Tree and etc. can only provide the capability for current and future query. None of them can provide a strategy for indexing the past, current and also the future information of moving objects. In this paper, we propose the past current future Index (PCFI Index) to index the past, current & future information of the moving objects. It is the combination of SETI tree and TPR * tree, the SETI liking index is used for indexing the historical trajectory segments except the front line structure, and the moving objects' current positions, velocities are indexed via the in memory frontline structure which mainly implemented with TPR * tree. Considering the large update operations on TPR tree of large population, a hash table considering cache sensitivity is also introduced. It works with the frontline part, leading a bottom up update of the tree. The performance analysis proves that the PCFI index can handle most of the query efficiently and provides a uniform solution for the trajectory query, time slice query, internal query and moving query.