一种障碍空间中的反k最近邻查询方法

随着基于位置的服务(LBS)和物联网的快速发展,空间查询技术越来越重要,而空间查询中的最近邻查询及其各种变体有着广泛的应用.近几年,已有较多对于查询前k个反最近邻对象(RkNN)的研究,其中大部分针对的都是理想欧氏空间.而在真实的情况下,反k最近邻查询通常受障碍物影响.文中研究了障碍空间中反k最近邻查询算法,提出了一种...     

空间网络数据库中反k最近邻查询算法

在空间网络数据库中,对象的位置和运动被约束在网络中,对象之间的距离不是传统的欧氏距离,而是由网络连通性决定的网络距离,因此,基于欧氏空间的反最近邻查询算法不适用于空间网络数据库.本文对空间网络数据库中的反最近邻查询问题进行了研究.给出网络数据和兴趣点的索引结构及空间网络数据存储模型.给出查询空间修剪定理,并在此基础上,提出空间网络数据库中适用于单、双色反七最近邻查询的RkNN算法.证明了该算法的正确性.最后通过实验对算法进行了验证.     

面向不确定数据的概率障碍k聚集最近邻查询

针对现有方法无法有效处理不确定数据的障碍k聚集最近邻查询问题的不足,提出了基于不确定Voronoi图的概率障碍k聚集最近邻查询(probabilistic obstacle k aggregate nearest neighbor query,POk ANN)方法。该方法分为3个阶段,分别是查询点集处理阶段、过滤阶段和精炼阶段。在处理阶段,计算查询点集的最小覆盖圆圆心q,为剪枝做准备。过滤阶段针对3种聚集函数设计了不同的过滤算法,去除不可能成为结果的数据点进而得到候选集合。精炼阶段将候选集合中概率值大于给定阈值的k个数据点集合存入结果集合并返回给用户。理论研究和实验表明,所提出的方法在概率障碍k聚集最近邻查询方面有明显的优势。     

RTC树的构建与不确定近邻关系查询方法

空间索引结构和查询技术在空间数据库中具有重要的作用,针对已有的方法在复杂空间数据对象的近似和组织方面的局限性,提出了一种基于最小外接矩形(MBR)、梯形和圆的新的索引结构(RTC树).为了有效处理复杂空间数据对象的最近邻(NN)关系查询问题,提出了基于RTC树的最近邻查询(NNRTC)算法,NNRTC算法利用剪枝规则可减少节点遍历和距离计算.针对障碍物对数据集中最近邻的影响问题,提出了障碍物环境下的基于RTC树的最近邻查询(BNNRTC)算法,BNNRTC算法先在理想空间进行查询,再对查询结果进行判断.为了有效处理动态单纯型连续近邻链查询问题,进一步给出了基于RTC树的动态单纯型连续近邻链查询(SCNNCRTC)算法.实验结果表明,相对基于R树的查询方法,所提的方法在处理数据量较大的复杂空间对象的数据集时可提高60%~80%的效率.     

基于kNN-DBSCAN的缺失数据填补优化算法

针对传统的kNN(k-NearestNeighbor)近邻填补算法对缺失数据的填补效果会因为k最近邻数据存在噪声受到较大干扰的问题,提出一种基于kNN-DBSCAN(k-NearestNeighbor Density-based Spatial Clustering of Applications with Noise)的缺失数据填补优化算法。将基于密度的DBSCAN聚类算法运用到kNN近邻填补算法中,先用kNN算法得到目标填补数据的原始k最近邻数据集,运用DBSCAN聚类算法对原始k最近邻数据集进行噪声检测并消除噪声数据,得到当前k最近邻数据集,最后并入kNN计算,填补目标缺失数据;同时,针对DBSCAN聚类算法参数设置敏感的问题,通过分析数据集的统计特性来确定参数,避免人为经验判断。最后利用真实数据对算法进行验证,结果显示该算法对目标缺失数据的填补准确度要优于传统的kNN算法。     

空间数据库中混合数据组最近邻查询

现有的组最近邻查询方法主要将空间中数据对象抽象为点或线段进行处理。但在现实应用中,仅仅将空间对象抽象为点或者线段,往往会影响查询的精度及效率。针对现有的组最近邻查询方法无法直接有效地处理混合数据组最近邻查询的不足,提出空间数据库中混合数据组最近邻查询方法。首先提出了混合数据Voronoi图的概念和性质。接着基于混合数据Voronoi图对混合数据集进行剪枝,针对查询对象数量为1和查询对象数量大于1的情况分别给出了相应的剪枝算法。利用所提的剪枝算法能有效去除不可能成为结果的数据对象,得到候选集合。在精炼过程中根据各个数据对象之间的位置关系给出相应的距离计算方法,通过比较候选集中数据对象到各个查询对象的距离之和,最终得到正确的查询结果。理论研究和实验表明,所提算法能够准确、有效地处理混合数据组最近邻查询问题。     

面向存在不确定对象的组最近邻查询方法

组最近邻查询是空间对象查询领域的一类重要查询,通过该查询可找到距离给定查询点集最近的空间对象.由于图像分辨率或解析度的限制等因素,空间对象的存在不确定性广泛存在于某些涉及图像处理的查询应用中.这些对象位置数据的存在不确定性会对组最近邻查询结果产生影响.本文给出面向存在不确定对象的概率阈值组最近邻查询定义,设计了高效的查询处理机制,通过剪枝优化等手段提高概率阈值组最近邻查询效率,并进一步提出了高效概率阈值组最近邻查询算法.采用多个真实数据集对概率阈值组最近邻算法进行了实验验证,结果表明所提算法具有良好的查询效率.     

外包空间数据库中反向k近邻查询验证

在外包空间数据库模式下,数据持有者委托第三方数据发布者代替它来管理数据并且执行查询.当发布者受到攻击或者由于自身的不安全性,它可能返回不正确的查询结果给用户.基于已有的反向k近邻(ReversekNearest Neighbor,RkNN)查询方法,采用将反向k近邻查询验证转化成k近邻查询验证和范围查询验证的思想,提出一种反向k近邻查询验证的方法,并且设计了相应的算法,用于验证返回给客户端结果的正确性(没有结果点被篡改),有效性(结果点都满足用户的查询要求)和完整性(没有遗漏符合查询要求的结果点).实验验证了算法的有效性和实用性.     

改进的全局最近邻查询算法及其在空间数据库中的应用

针对基础数据集合和参考数据集合为相同数据集的情况,给出了一种新型的空间数据库全局最近邻查询算法,该算法能够利用数据最近邻结果的自身特点,避免较大的查询过程中的资源开销.实验结果表明,算法的效率要明显优于常用最近邻查询算法,并且对于不同的数据维数和数据元素数量,特别是对于高维的空间数据集合,算法具有较高的稳定性.     

一种对任意k的求解RkNN方法的研究

给定集合P和查询对象q,RNN返回的是P的子集P’,且P’中的任意对象o到q的距离都比o到P中的其他对象的距离小,即集合P’中的所有对象都以q为最近邻。本文了介绍一种求任意k的RkNN方法,并对该方法进行改进。     

移动对象的反向k近邻查询算法的研究

传统的反向k近邻查询的研究主要集中在k=1时的单色移动对象的反向最近邻查询上,单色和双色的反向k近邻查询问题还没有解决。利用网格索引结构结合60°平面修剪策略,提出了一种解决单色和双色的移动对象的连续反向k近邻查询方法。最后实验结果验证了算法的有效性。     

Reverse Approximate Nearest Neighbor Queries on Road Network

World Wide Web - Reverse k Nearest Neighbor (RkNN) queries retrieve all objects that consider the query as one of their k most influential objects. Given a set of user U, a set of facilities F and...     

Ranked Reverse Nearest Neighbor Search

Given a set of data points P and a query point q in a multidimensional space, reverse nearest neighbor (RNN) query finds data points in P whose nearest neighbors are q. Reverse k-nearest neighbor (RkNN) query (where k ges 1) generalizes RNN query to find data points whose kNNs include q. For RkNN query semantics, q is said to have influence to all those answer data points. The degree of q's influence on a data point p (isin P) is denoted by kappap where q is the kappap-th NN of p. We introduce a new variant of RNN query, namely, ranked reverse nearest neighbor (RRNN) query, that retrieves t data points most influenced by q, i.e., the t data points having the smallest kappa's with respect to q. To answer this RRNN query efficiently, we propose two novel algorithms, kappa-counting and kappa-browsing that are applicable to both monochromatic and bichromatic scenarios and are able to deliver results progressively. Through an extensive performance evaluation, we validate that the two proposed RRNN algorithms are superior to solutions derived from algorithms designed for RkNN query.     

时空数据库中多个最近邻对象的查询算法

在时空数据库中，最近邻查询用于对某个查询对象，在被查询对象中找出离它最近的一个或多个对象。该文在TPR树这一时空索引的基础上，提出了一种高效的最近邻查询算法，能够支持移动对象的多个最近邻对象的查询，并在性能上也有所提高。     

Processing moving queries over moving objects using motion-adaptive indexes

This paper describes a motion-adaptive indexing scheme for efficient evaluation of moving continual queries (MCQs) over moving objects. It uses the concept of motion-sensitive bounding boxes (MSBs) to model moving objects and moving queries. These bounding boxes automatically adapt their sizes to the dynamic motion behaviors of individual objects. Instead of indexing frequently changing object positions, we index less frequently changing object and query MSBs, where updates to the bounding boxes are needed only when objects and queries move across the boundaries of their boxes. This helps decrease the number of updates to the indexes. More importantly, we use predictive query results to optimistically precalculate query results, decreasing the number of searches on the indexes. Motion-sensitive bounding boxes are used to incrementally update the predictive query results. Furthermore, we introduce the concepts of guaranteed safe radius and optimistic safe radius to extend our motion-adaptive indexing scheme to evaluating moving continual k-nearest neighbor (kNN) queries. Our experiments show that the proposed motion-adaptive indexing scheme is efficient for the evaluation of both moving continual range queries and moving continual kNN queries.     

Density-based spatial keyword querying

With the rocket development of the Internet, WWW(World Wide Web), mobile computing and GPS (Global Positioning System) services, location-based services like Web GIS (Geographical Information System) portals are becoming more and more popular. Spatial keyword queries over GIS spatial data receive much more attention from both academic and industry communities than ever before. In general, a spatial keyword query containing spatial location information and keywords is to locate a set of spatial objects that satisfy the location condition and keyword query semantics. Researchers have proposed many solutions to various spatial keyword queries such as top-K keyword query, reversed kNN keyword query, moving object keyword query, collective keyword query, etc. In this paper, we propose a density-based spatial keyword query which is to locate a set of spatial objects that not only satisfies the query’s textual and distance condition, but also has a high density in their area. We use the collective keyword query semantics to find in a dense area, a group of spatial objects whose keywords collectively match the query keywords. To efficiently process the density based spatial keyword query, we use an IR-tree index as the base data structure to index spatial objects and their text contents and define a cost function over the IR-tree indexing nodes to approximately compute the density information of areas. We design a heuristic algorithm that can efficiently prune the region according to both the distance and region density in processing a query over the IR-tree index. Experimental results on datasets show that our method achieves desired results with high performance.     

BORDER: efficient computation of boundary points

This work addresses the problem of finding boundary points in multidimensional data sets. Boundary points are data points that are located at the margin of densely distributed data such as a cluster. We describe a novel approach called BORDER (a BOundaRy points DEtectoR) to detect such points. BORDER employs the state-of-the-art database technique - the Gorder kNN join and makes use of the special property of the reverse k nearest neighbor (RkNN). Experimental studies on data sets with varying characteristics indicate that BORDER is able to detect the boundary points effectively and efficiently.     

基于对象数量的宽度加权聚类kNN算法

传统k最近邻算法（k-Nearest Neighbor,kNN）作为一种非参数化分类技术在数据分析中具有广泛的应用,但该算法具有较多的冗余计算,致使处理数据时需要花费较多的计算时间。目前大量的研究都集中在数据的预处理阶段,通过为数据建立模型降低kNN查询的计算量。提出一种基于对象数量的宽度加权聚类kNN算法（NOWCkNN）,该算法中数据集首先以全局宽度进行聚类,每个生成的子集群根据其对象数量递归计算其宽度的权值,然后算法根据其权值的大小和调和系数调节宽度值,最后生成不同宽度大小的集群用于kNN查询。这不仅减少了算法的聚类时间,还能平衡产生集群的大小,减少迭代次数,使三角不等式修剪率达到最大。实验结果表明,NOWCkNN算法与现有工作相比在各个维度的数据集中有较好的性能,尤其是在高维度、数据量较大的数据集中有更高的修剪效率。     

VGQ-Vor: extending virtual grid quadtree with Voronoi diagram for mobile k nearest neighbor queries over mobile objects

Performing mobile k nearest neighbor (MkNN) queries whilst also being mobile is a challenging problem. All the mobile objects issuing queries and/or being queried aremobile. The performance of this kind of query relies heavily on the maintenance of the current locations of the objects. The index used for mobile objects must support efficient update operations and efficient query handling. This study aims to improve the performance of the MkNN queries while reducing update costs. Our approach is based on an observation that the frequency of one region changing between being occupied or not by mobile objects is much lower than the frequency of the position changes reported by the mobile objects. We first propose an virtual grid quadtree with Voronoi diagram(VGQ-Vor), which is a two-layer index structure that indexes regions occupied by mobile objects in a quadtree and builds a Voronoi diagram of the regions. Then we propose a moving k nearest neighbor (kNN) query algorithm on the VGQ-Vor and prove the correctness of the algorithm. The experimental results show that the VGQ-Vor outperforms the existing techniques (Bx-tree, Bdual-tree) by one to three orders of magnitude in most cases.