共查询到17条相似文献,搜索用时 234 毫秒
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针对TPR*-tree隐含移动对象部分最近历史信息但不能提供历史信息查询的问题,将移动对象创建或更新时间引入到索引树中,提出一种既支持预测查询又支持部分历史信息查询的索引树Basic HTPR*-tree,为全时态查询奠定了坚实的基础.同时,为了支持移动对象的频繁更新,在Basic HTPR*-tree索引树基础上引入内存概要结构和Hash辅助索引结构,提出支持自底向上更新策略的HTPR*-tree索引结构.实验结果表明,HTPR*-tree更新性能优于TPR*-tree和Basic HTPR*-tree(TD_HTPR*-tree),预测查询性能仅仅稍逊于TPR*-tree. 相似文献
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《计算机学报》2014,(9)
为了支持对大规模不确定性移动对象当前及将来位置的查询,亟需设计更加有效和高效的索引结构.当前索引算法主要考虑索引建立和维护的效率问题或关注基于索引进行查询时的准确性,对索引建立维护以及查询时性能综合考虑的研究较少.针对已有方法的不足,提出基于路网的移动对象动态双层索引结构DISC-tree,对静态路网信息采用R~*-tree索引,对实时更新的移动对象运动轨迹采用结点更新代价较小的R-tree进行索引,设计哈希表和双向链表辅助结构对索引协同管理.成都市真实地图数据集上的实验结果表明:相比于经典的NDTRtree,DISC-tree在索引建立和维护方面时间代价平均减少39.1%,移动对象轨迹查询时间代价平均减少24.1%;相比于FNR-tree,DISC-tree的范围查询准确率平均提高约31.6%. 相似文献
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一种基于 R* -tree的时空索引 总被引:5,自引:1,他引:5
时空数据是一种特殊的多维数据,其每一数据项的时间戳值是单调递增的。3D R-tree把时间看作为空间的另一维,然后利用R-tree进行空间索引,查询效率比较低而且只能处理离线数据。而HR-tree将时间维孤立出来,同时保存每一时间戳的空间数据,查询效率较高但是空间开销大。该文提出了一种基于R*-tree算法的时空索引方法。该方法比3D R-tree有更好的查询效率且支持在线数据模式.较之HR-tree在保证查询效率的同时使用更少的存储空间,最后给出了试验对比结果。 相似文献
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在给定的空间及时间范围内,如何构建高效的时空索引结构,以实现对移动对象快速有效的检索,是实现定位服务、智能交通、数字化战争等诸多应用中所迫切需要解决的问题.本文依据移动对象的运动特点,提出了一种面向当前及将来时刻快速更新及有效检索的索引结构—PQR树.PQR树是综合PMRQuad树和R*树的结构,首先依据道路分布用PMRQuad树将移动对象的索引空间实行粗略的层分割,将所有快速移动对象与道路相关联.然后用R*树索引分布在各个子空间块内的类静止对象.实验结果表明PQR树具有良好的更新和查询性能. 相似文献
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为提高多元时间序列相似查询执行效率,采用了基于距离索引结构的相似查询算法。利用主成分分析方法对多元时间序列数据降维并在此基础上进行聚类,以聚类质心为参考点,将各类变换到一维空间,利用B+-tree结构进行索引查询,找到与查询序列最相似的k个MTS序列。实验表明查询效率和准确性都有比较大的提高。 相似文献
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Moving object databases are required to support different types of queries with a large number of moving objects. New types of queries namely directions and velocity queries (DV queries), are to be supported and covered. The TPR-tree and its successors are efficient indexes that support spatio-temporal queries for moving objects. However, neither of them support the new DV queries. In this paper, we propose a new index for moving objects based on the TPR*-tree, named Direction and Velocity of TPR*-tree or DV-TPR*-tree, in order to build data a structure based on the spatial, direction and velocity domains. DV-TPR*-tree obtains an ideal distribution that supports and fulfils the new query types (DV queries). Extensive performance studies show that the query performance of DV-TPR*-tree outperforms the TPR-tree and its successors. 相似文献
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Effectiveness of NAQ-tree in handling reverse nearest-neighbor queries in high-dimensional metric space 总被引:1,自引:1,他引:0
Reverse nearest-neighbor (RNN) query processing is important for many applications such as decision-support systems, profile-based
marketing and molecular biology; consequently, RNN query processing has attracted considerable attention in the research community
in recent years. Most existing approaches for RNN query processing either rely on nearest-neighbor pre-computation or work
for specific data space (e.g., the Euclidean space). The only method for RNN query processing in metric space is based on
the M-tree. In this paper, we propose an approach for RNN query processing in high-dimensional metric space using distance-based
index structure (in particular, NAQ-tree that outperforms the other distance-based index structures as we have already verified in a previous study). In high-dimensional
space, the properties of distance-based index structure provide strong pruning rules than the M-tree. In addition, unlike the previous work, our approach integrates the filtering and verification steps and uses the information
obtained in the verification stage to further improve the filtering rate. Our approach delivers results incrementally and
hence well serves real-time applications. The reported experimental results demonstrate the applicability and effectiveness
of the proposed NAQ-tree-based RNN approach. 相似文献
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索引技术是时态数据库查询优化的重要方法之一。本文提出的可变Hash(VH)索引是建立在时间属性上的一种新的动态索引技术,主要目的是提高时态数据库快照查询的效率。由于时间的不确定性,在时态数据的时间属性上建立Hash索引比较困难。VH索引克服了Hash索引这一难点,提出了索引参数可变的思想,并应用B^+-树对Hash参数进行组织。查询时由时间值在B^+-树上获得Hash参数,进而确定数据的存储地址。通过对其时间复杂度和空间复杂度的理论分析以及实验验证,表明该索引技术可以减少索引查找以及读取数据的I/O次数,并具有理想的空间利用率。 相似文献
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An efficient index structure for complex multi-dimensional objects is one of the most challenging requirements in non-traditional applications such as geographic information systems, computer-aided design, and multimedia databases. In this paper we first propose a main memory data structure for complex multi-dimensional objects. Then, we present an extension of the existing multi-dimensional index structure. Among existing multi-dimensional index structures, the popular R*-tree is selected. The R*-tree is coupled with the main memory data structure to improve the performance of spatial query processing. An analytical model is developed for our index structure. Experimental results show that the analytical model is accurate, the relative error being below 15%. The performance of our index structure is compared with that of a state-of-the-art index structure by experimental measurements. Our index structure outperforms the state-of-the-art index structure due to its ability to reduce a large amount of storage. 相似文献
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传统Top-[k]空间关键字查询忽略了兴趣对象周围的基础设施属性对于用户偏好的影响,针对该问题,研究了基于影响区域约束关系的Top-[k]空间关键字偏好查询问题,设计了一种基于贪心策略的最近邻算法GS-NNA(Greedy Strategy based Nearest Neighbor Algorithm)。该算法采用R*-tree和倒排文件两种索引结构,结合贪心思想和最近邻算法,每次选择分值最高的兴趣对象作为候选结果集,并利用阈值判定条件对R*-tree进行剪枝。实验结果表明,GS-NNA算法与现有相关算法相比,有效提高了查询效率。 相似文献
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In this paper we propose and analyze a new spatial access method, namely the S*-tree, for the efficient secondary memory encoding and manipulation of images containing multiple non-overlapping features (i.e., coloured images). The S*-tree is based on a non-straightforward and space efficient extension to coloured images of its precursor, namely the S+-tree, which was explicitly designed for binary images. To assess experimentally the qualities of the S*-tree, we test it against the HL-quadtree, a previous spatial access method for coloured images, which is known to be space and time efficient. Our experiments show that the S*-tree reaches up to a 75% of space saving, and performs constantly less I/O accesses than the HL-quadtree in solving classical window queries. 相似文献