连续最近邻查询方法研究

本文分析了目前进行连续最近邻查询的几种方法,并由该问题的几何特征入手,提出了基于R-tree的查询算法,可以避免分割点的丢失和高代价的查询,能够有效地完成移动对象的连续最近邻查询.     

In-Route Nearest Neighbor Queries

Nearest neighbor query is one of the most important operations in spatial databases and their application domains, such as location-based services and advanced traveler information systems. This paper addresses the problem of finding the in-route nearest neighbor (IRNN) for a query object tuple which consists of a given route with a destination and a current location on it. The IRNN is a facility instance via which the detour from the original route on the way to the destination is smallest. This paper addresses four alternative solution methods. Comparisons among them are presented using an experimental framework. Extensive experiments using real road map datasets are conducted to examine the behaviors of the solutions in terms of five parameters affecting the performance. The overall experiments show that our strategy to reduce the expensive path computations to minimize the response time is reasonable. The spatial distance join-based method always shows better performance with fewer path computations compared to the recursive methods. The computation costs for all methods except the precomputed zone-based method increase with increases in the road map size and the query route length but decrease with increases in the facility density. The precomputed zone-based method shows the most efficiency when there are no updates on the road map.     

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.     

多对象的最近邻查询

最近邻查询是地理信息系统等领域经常遇到的问题,该文在最近邻查询的基础上提出一种多个对象的最近邻查询,并利用已有的R-树最近邻查询方法实现多个对象的查询,该方法不同于普通的最近邻查询,是一种新的方法,在实际应用中也很有意义。     

等和值块扩展最近邻矢量量化码字搜索算法

论文提出一种等和值块扩展最近邻矢量量化码字搜索算法。该算法将码书按和值大小排序分块,并将每一块中间或中间附近的码字的和值作为本码书块的特征和值。编码时,查找与输入矢量和值距离最近的码书块并作为初始匹配码书块。然后在该码书块附近上下扩展搜索相邻码书块中距输入矢量最近的码字。该算法具有无复杂运算的特点,易于VLSI技术实现。仿真结果表明,该算法是一种有效的码字搜索算法。     

Nearest Neighbor Queries in Shared-Nothing Environments

In this paper, we propose an efficient solution to the problem of nearest neighbor query processing in declustered spatial databases. Recently a branch-and-bound nearest neighbor finding (BB-NNF) algorithm has been designed to process nearest neighbor queries in R-trees. However, this algorithm is strictly serial (branch-and-bound oriented) and its performance degrades, during processing of a nearest neighbor query, if applied to a parallel environment, since it does not exploit any kind of parallelization. We develop an efficient query processing strategy for parallel nearest neighbor finding (P-NNF), assuming a shared nothing multi-processor architecture, where the processors communicate via a network. In our method, the relevant sites are activated simultaneously. In order to achieve this goal, statistical information is used. The efficiency measure is the response time of a given query. Experimental results, based on real-life and synthetic datasets, show that the proposed method outperforms the branch-and-bound method by factors.     

基于概念划分的连续最近邻查询研究

CPM(conceptual partitioning monitoring)是一种较为高效的概念划分网格的思想,用以解决二维空间下的连续最近邻查询问题.在此思想的基础上提出一种采用树形结构来索引概念划分网格的连续最近邻查询算法T-CPM,通过一系列改进步骤,提升了这一算法的查询效率.实验证明,相比经典的算法,T-CPM优化了网格的检索顺序并节省了计算代价.此外,验证了将这一新的方法延伸到基于不确定空间对象的连续最近邻查询问题中,以此给出了一种针对动态不确定空间数据最近邻查询问题的思路和方法.     

基于平面线段的反向最近邻查询

反向最近邻查询是空间数据库空间查询的研究热点。目前反向最近邻查询的查询粒度都是基于一维的点,在一些空间物体不能抽象为点的情况下将其抽象为点进行反向最近邻查询,查询结果不能达到一定的精度。该文在分析基于平面线段的最近邻查询和R树结构的基础上提出了一种改进的R树—Rcd树,并给出了基于Rcd树的平面线段反向最近邻查询算法,该方法能实现平面线段的反向最近邻查询。     

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...     

基于地表的连续可见最近邻查询方法

连续可见最近邻查询是查询连续空间的最近邻问题,目前的研究基本以二维空间为背景并提出了一些查询算法,但可见性判断方法不能适用于三维或高维空间.以陆地表面的三维数据为研究背景,提出了一种查询地表任意路径的连续可见最近邻方法.该方法以计算步长的方式把整个查询路径分割成若干个连续的查询子路径,循环计算每个子路径的连续可见最近邻直至得到整个路径的查询结果.该方法可以扩展应用于高维空间中的连续最近邻查询.     

Selective Sampling for Nearest Neighbor Classifiers

Most existing inductive learning algorithms work under the assumption that their training examples are already tagged. There are domains, however, where the tagging procedure requires significant computation resources or manual labor. In such cases, it may be beneficial for the learner to be active, intelligently selecting the examples for labeling with the goal of reducing the labeling cost. In this paper we present LSS—a lookahead algorithm for selective sampling of examples for nearest neighbor classifiers. The algorithm is looking for the example with the highest utility, taking its effect on the resulting classifier into account. Computing the expected utility of an example requires estimating the probability of its possible labels. We propose to use the random field model for this estimation. The LSS algorithm was evaluated empirically on seven real and artificial data sets, and its performance was compared to other selective sampling algorithms. The experiments show that the proposed algorithm outperforms other methods in terms of average error rate and stability.     

基于平面线段的反向最近邻查询

反向最近邻查询是空间数据库空间查询的研究热点。目前反向最近邻查询的查询粒度都是基于一维的点．在一些空间物体不能抽象为点的情况下将其抽象为点进行反向最近邻查询,查询结果不能达到一定的精度。该文在分析基于平面线段的最近邻查询和R树结构的基础上提出了一种改进的R树－Rcd树,并给出了基于Rcd树的平面线段反向最近邻查询算法．该方法能实现平面线段的反向最近邻查询。     

基于GPU的K-近邻算法实现

K-近邻计算在数据集规模较大时计算复杂度较高,因此,利用图形处理器( GPU )强大的并行计算能力对K-近邻算法进行加速。在分析现有K-近邻算法的基础上,针对该算法时间开销过大的问题,结合GPU的体系结构特征实现基于GPU的K-近邻算法。利用全局存储器的合并访问特性,提高GPU全局存储器访问数据的效率,通过事先过滤数据的方法来减少参与排序的数据量,进而减少排序阶段的线程串行化时间。在 KDD, Poker, Covertype 3个数据集上进行实验,结果表明,该实现方法在距离计算阶段每秒执行的浮点运算次数为266.37×109次,而排序阶段为26.47×109次,优于已有方法。     

Reverse Nearest Neighbor Search in Metric Spaces

Given a set {cal D} of objects, a reverse nearest neighbor (RNN) query returns the objects o in {cal D} such that o is closer to a query object q than to any other object in {cal D}, according to a certain similarity metric. The existing RNN solutions are not sufficient because they either 1) rely on precomputed information that is expensive to maintain in the presence of updates or 2) are applicable only when the data consists of "Euclidean objects” and similarity is measured using the L_2 norm. In this paper, we present the first algorithms for efficient RNN search in generic metric spaces. Our techniques require no detailed representations of objects, and can be applied as long as their mutual distances can be computed and the distance metric satisfies the triangle inequality. We confirm the effectiveness of the proposed methods with extensive experiments.     

变权值下的最近相邻检索策略

最近相邻策略是基于事例推理(CBR)中常用的检索策略。针对该方法的缺点该文提出了变权值的最近相邻检索,并探讨了变权值带来的问题,在此基础上该文给出了两种解决方法,事例检索记忆表和采用神经网络与最近相邻策略相结合的方法来检索相似源事例,可在变权值的情况下快速地检索出相关的源事例。从而解决了事例库的设计者和使用者之间的由视角不同而产生的矛盾。     

基于动态距离的近邻查询算法研究

移动对象轨迹数据的挖掘是移动对象数据库的一个重要研究方向,从轨迹数据中得到的信息可以应用到交通控制、气候预测以及生态研究等多个方面.基于轨迹数据形式化定义时态距离,用相对简单的近似距离替代精确距离来减少查询过程中的计算量.在关系模型和树结构模型上,实现基于动态距离的距离查询、时间点最近邻查询和时间段最近邻查询算法并对查询效率加以比较.实验结果表明,树模型的查询效率明显高于关系模型.最后在树结构模型中引入嵌入式索引进一步提高了查询效率.     

基于数据网格环境的k近邻查询

提出一种在网格环境下的k近邻查询方法——GkNN．到目前为止,尚未有文献提出数据网格环境下的k近邻查询算法．当用户在查询节点提交一个查询向量和k,首先以一个较小的查询半径。在数据节点进行基于双重距离尺度的向量缩减,然后将缩减后的向量按照向量“打包”传输的方式发送到执行节点,在执行节点并行地对这些候选向量进行距离（求精）运算．最终将结果向量返回到查询节点．当返回的向量个数小于k时,扩大半径值,继续循环直到得到k个最近邻向量为止．理论分析和实验证明该方法在减少网络通信开销、增加I／O和CPU并行、降低-向应时间方面具有较好的性能,非常适合海量高维数据的查询．     

Algorithms for Nearest Neighbor Search on Moving Object Trajectories

Nearest Neighbor (NN) search has been in the core of spatial and spatiotemporal database research during the last decade. The literature on NN query processing algorithms so far deals with either stationary or moving query points over static datasets or future (predicted) locations over a set of continuously moving points. With the increasing number of Mobile Location Services (MLS), the need for effective k-NN query processing over historical trajectory data has become the vehicle for data analysis, thus improving existing or even proposing new services. In this paper, we investigate mechanisms to perform NN search on R-tree-like structures storing historical information about moving object trajectories. The proposed (depth-first and best-first) algorithms vary with respect to the type of the query object (stationary or moving point) as well as the type of the query result (historical continuous or not), thus resulting in four types of NN queries. We also propose novel metrics to support our search ordering and pruning strategies. Using the implementation of the proposed algorithms on two members of the R-tree family for trajectory data (namely, the TB-tree and the 3D-R-tree), we demonstrate their scalability and efficiency through an extensive experimental study using large synthetic and real datasets.

不确定数据上范围受限的最近邻查询算法

不确定数据的查询处理是数据库领域近年来的热点研究课题.提出一种不确定数据上的范围受限的最近邻查询.给定不确定数据集D={o1,o2,…,on},范围约束R是一个简单多边形,q为一固定的查询点,范围受限的最近邻查询返回的是在数据集D中,既满足范围约束R,又能成为查询点q的最近邻的对象集合.为处理该查询,提出了范围受限的最近邻核心集的概念和范围受限的最近邻核心集的查找算法.并提出一种计算范围受限的最近邻候选集的优化方法,降低了查询代价.最后通过实验验证了该算法的有效性.     

连续近邻查询方法的研究

连续近邻查询(CNN)要检索一给定查询线段上每一点的近邻。它是时空数据库中一种重要的查询类型,在智能交通系统中有着广泛的应用。Voronoi图解决连续近邻查询问题,思想简单明晰,但Voronoi图构造代价太高,尤其是高阶的Voronoi图。本文从文献得到启示:用分枝限界的思想去界定预创建Voronoi图生成点范围的上限。提出了一种动态地创建局部Voronoi图的办法解决连续近邻查询问题。这种方法只是在给定查询段上所有点的k个近邻范围上限内创建一个局部的k阶Voronoi图,这样会大大降低基于Voronoi图的连续k近邻查询的代价。