Continuous visible nearest neighbor query processing in spatial databases

In this paper, we identify and solve a new type of spatial queries, called continuous visible nearest neighbor (CVNN) search. Given a data set P, an obstacle set O, and a query line segment q in a two-dimensional space, a CVNN query returns a set of \({\langle p, R\rangle}\) tuples such that \({p \in P}\) is the nearest neighbor to every point r along the interval \({R \subseteq q}\) as well as p is visible to r. Note that p may be NULL, meaning that all points in P are invisible to all points in R due to the obstruction of some obstacles in O. In contrast to existing continuous nearest neighbor query, CVNN retrieval considers the impact of obstacles on visibility between objects, which is ignored by most of spatial queries. We formulate the problem, analyze its unique characteristics, and develop efficient algorithms for exact CVNN query processing. Our methods (1) utilize conventional data-partitioning indices (e.g., R-trees) on both P and O, (2) tackle the CVNN search by performing a single query for the entire query line segment, and (3) only access the data points and obstacles relevant to the final query result by employing a suite of effective pruning heuristics. In addition, several interesting variations of CVNN queries have been introduced, and they can be supported by our techniques, which further demonstrates the flexibility of the proposed algorithms. A comprehensive experimental evaluation using both real and synthetic data sets has been conducted to verify the effectiveness of our proposed pruning heuristics and the performance of our proposed algorithms.     

空间对象的反最近邻查询处理技术研究

空间数据库中反最近邻查询的研究是空间查询的研究热点。在对现有的反最近邻查询技术进行分析比较的基础上,针对提高动态数据集的查询效率问题,给出了基于R树索引结构的反最近邻查询方案。通过实验结果的分析比较,可以看出该方案能够有效地解决动态数据集的查询问题。     

DART+: Direction-aware bichromatic reverse k nearest neighbor query processing in spatial databases

This article presents a novel type of queries in spatial databases, called the direction-aware bichromatic reverse k nearest neighbor(DBRkNN) queries, which extend the bichromatic reverse nearest neighbor queries. Given two disjoint sets, P and S, of spatial objects, and a query object q in S, the DBRkNN query returns a subset P′ of P such that k nearest neighbors of each object in P′ include q and each object in P′ has a direction toward q within a pre-defined distance. We formally define the DBRkNN query, and then propose an efficient algorithm, called DART, for processing the DBRkNN query. Our method utilizes a grid-based index to cluster the spatial objects, and the B⁺-tree to index the direction angle. We adopt a filter-refinement framework that is widely used in many algorithms for reverse nearest neighbor queries. In the filtering step, DART eliminates all the objects that are away from the query object more than a pre-defined distance, or have an invalid direction angle. In the refinement step, remaining objects are verified whether the query object is actually one of the k nearest neighbors of them. As a major extension of DART, we also present an improved algorithm, called DART+, for DBRkNN queries. From extensive experiments with several datasets, we show that DART outperforms an R-tree-based naive algorithm in both indexing time and query processing time. In addition, our extension algorithm, DART+, also shows significantly better performance than DART.     

移动对象反向最近邻查询处理技术研究进展

随着移动通信技术的快速发展和个人移动通信终端功能的不断完善,移动计算技术有了更加广阔的应用背景,尤其是移动对象的反向最近邻查询处理技术得到了研究人员的广泛关注。对近几年提出的移动对象反向最近邻查询方法进行了研究,根据其查询处理过程,将反向最近邻查询方法分为基于预处理的方法和基于空间修剪的方法;总结了近年来提出的有效解决方法和研究进展,最后介绍了移动对象反向最近邻查询处理技术的最新发展趋势。     

基于NNlists的路网k路径近邻查询

为满足k路径近邻查询的实时性要求,运用预计算思想提出了基于NNlists的BNNL算法,通过在用户当前位置和目的地结点进行双向Dijkstra扩展得到两点间的最短路径,再通过对最短路径上的路网结点预计算的m近邻进行优化处理,最终得到正确的k路径近邻。该方法提高了k路径近邻查询的查询速度,尤其适用于兴趣点密度较大、k值较大的情况。     

基于查询集空间分布的聚合最近邻查询算法

聚合最近邻查询涉及到多个查询对象,因此比传统最近邻查询更复杂,而且其查询集空间分布特征暗含了查询集聚合最近邻的区域分布信息。充分考虑查询集分布特征,给出了利用分布特征指导聚合最近邻搜索的方法,并以此提出了一种新的聚合最近邻查询算法——AM算法。AM算法能动态地捕捉并利用查询集空间分布特征,使得对数据点的搜索按正确的次序进行,避免对不必要数据点的搜索。最后通过实验验证了AM算法的高效性。     

Probabilistic nearest neighbor query processing on distributed uncertain data

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost.     

基于概率的反向K最近邻高效查询算法研究

反向K最近邻查询需要确定以给定查询对象作为其k个最近邻之一的所有对象。然而由于大量应用需要处理未知数据,人们迫切需要能够处理未知对象的新算法。这里的主要问题是,一个对象属于RKNN结果集的事件不再是一个确定性事件,而是一个以一定概率成立的随机变量。对基于概率论的未知数据集反向K最近邻(PRKNN)搜索问题展开研究,以足够大的概率返回以查询对象为其最近邻的未知对象。基于一种新的考虑了距离相关性的修剪机制,提出一种PRNN高效查询算法。此外,还给出了如何将该算法扩展至PRKNN(其中k>1)查询处理。最后,将该算法与当前其他最新算法作比较,实验评估结果表明,该算法性能明显优于其他算法。     

空间数据库中的障碍反向最近邻查询

反向最近邻查询已成为空间查询的热点问题,而障碍物在实际应用中是不可避免的,因而在障碍物环境中的反向最近邻查询也成为重要的空间查询。已有的可视反向最近邻查询只考虑了可视性,并没有考虑最小障碍距离。提出一种障碍物环境中新的反向最近邻查询的变体,查找障碍距离最小的反向最近邻,即障碍反向最近邻查询。利用障碍距离的计算和相应的剪枝规则,给出障碍反向最近邻查询的算法及相关定理和证明。     

距离-关键字相似度约束的双色反k近邻查询方法

针对空间关键字双色反k近邻查询返回结果质量较低的问题,提出了基于距离-关键字相似度约束的双色反k近邻查询方法.首先,通过设置一个阈值将查询结果中质量较低的用户给过滤掉,从而避免了查询结果中出现空间距离相对较远的用户,保证了查询结果质量;然后,为支持该查询,提出了一种关键字多分辨率网格矩形树(KMG-Tree)索引来管理...     

Efficient mutual nearest neighbor query processing for moving object trajectories

Given a set D of trajectories, a query object q, and a query time extent Γ, a mutual (i.e., symmetric) nearest neighbor (MNN) query over trajectories finds from D, the set of trajectories that are among the k₁ nearest neighbors (NNs) of q within Γ, and meanwhile, have q as one of their k₂ NNs. This type of queries is useful in many applications such as decision making, data mining, and pattern recognition, as it considers both the proximity of the trajectories to q and the proximity of q to the trajectories. In this paper, we first formalize MNN search and identify its characteristics, and then develop several algorithms for processing MNN queries efficiently. In particular, we investigate two classes of MNN queries, i.e., MNN_P and MNN_T queries, which are defined with respect to stationary query points and moving query trajectories, respectively. Our methods utilize the batch processing and reusing technology to reduce the I/O cost (i.e., number of node/page accesses) and CPU time significantly. In addition, we extend our techniques to tackle historical continuous MNN (HCMNN) search for moving object trajectories, which returns the mutual nearest neighbors of q (for a specified k₁ and k₂) at any time instance of Γ. Extensive experiments with real and synthetic datasets demonstrate the performance of our proposed algorithms in terms of efficiency and scalability.     

Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data

Reverse nearest neighbor (RNN) search is very crucial in many real applications. In particular, given a database and a query object, an RNN query retrieves all the data objects in the database that have the query object as their nearest neighbors. Often, due to limitation of measurement devices, environmental disturbance, or characteristics of applications (for example, monitoring moving objects), data obtained from the real world are uncertain (imprecise). Therefore, previous approaches proposed for answering an RNN query over exact (precise) database cannot be directly applied to the uncertain scenario. In this paper, we re-define the RNN query in the context of uncertain databases, namely probabilistic reverse nearest neighbor (PRNN) query, which obtains data objects with probabilities of being RNNs greater than or equal to a user-specified threshold. Since the retrieval of a PRNN query requires accessing all the objects in the database, which is quite costly, we also propose an effective pruning method, called geometric pruning (GP), that significantly reduces the PRNN search space yet without introducing any false dismissals. Furthermore, we present an efficient PRNN query procedure that seamlessly integrates our pruning method. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed GP-based PRNN query processing approach, under various experimental settings.     

Continuous visible k nearest neighbor query on moving objects

A visible k nearest neighbor (Vk NN) query retrieves k objects that are visible and nearest to the query object, where “visible” means that there is no obstacle between an object and the query object. Existing studies on the Vk NN query have focused on static data objects. In this paper we investigate how to process the query on moving objects continuously. We propose an effective filtering-and-refinement framework for evaluating this type of queries. We exploit spatial proximity and visibility properties between the query object and data objects to prune search space under this framework. A detailed cost analysis and a comprehensive experimental study are conducted on the proposed framework. The results validate the effectiveness of the pruning techniques and verify the efficiency of the proposed framework. The proposed framework outperforms a straightforward solution by an order of magnitude in terms of both communication and computation costs.     

On multi-type reverse nearest neighbor search

This paper presents a study of the Multi-Type Reverse Nearest Neighbor (MTRNN) query problem. Traditionally, a reverse nearest neighbor (RNN) query finds all the objects that have the query point as their nearest neighbor. In contrast, an MTRNN query finds all the objects that have the query point in their multi-type nearest neighbors. Existing RNN queries find an influence set by considering only one feature type. However, the influence from multiple feature types is often critical for strategic decision making in many business scenarios, such as site selection for a new shopping center. To that end, we first formalize the notion of the MTRNN query by considering the influence of multiple feature types. We also propose R-tree based algorithms to find the influence set for a given query point and multiple feature types. Finally, experimental results are provided to show the strength of the proposed algorithms as well as design decisions related to performance tuning.     

基于不确定数据的top-k概率相互最近邻查询*

不确定数据上的概率相互最近邻查询具有重要的实际应用,针对目前关于这方面的研究尚少,提出了不确定数据上的概率相互最近邻的top-k查询算法。首先对问题进行描述与定义,其次总结可行的裁剪规则,从而裁剪查询对象中未计算的实例点。通过实验表明,该算法能有效地降低最近邻查询中的I/O开销,提高查询的响应速度。     

路网中移动对象快照K近邻查询处理

扩展了一种支持路网中移动对象的位置相关查询框架的功能,利用存在磁盘上的R树来存储网络连通性和一种基于内存的网格结构来维持移动对象的位置更新,提出了基于范围查询(MNDR)的快照K近邻查询算法(SKNN),对空间中的任意一条边,分析可能受影响的最大数量和最小数量的网格单元格,说明用于快照范围查询处理的搜索空间的最大范围,预估包含查询结果的子空间,使用这个子空间作为范围调用MNDR来有效地计算路网中查询点的KNN POI,降低I/O成本,缩短查询时间。通过实验对比,当规模扩展到数十万的移动对象时,SKNN比种有效查询处理空间网络数据的预计算方法S-GRID有更好大的系统吞吐量。     

Instance optimal query processing in spatial networks

The performance optimization of query processing in spatial networks focuses on minimizing network data accesses and the cost of network distance calculations. This paper proposes algorithms for network k-NN queries, range queries, closest-pair queries and multi-source skyline queries based on a novel processing framework, namely, incremental lower bound constraint. By giving high processing priority to the query associated data points and utilizing the incremental nature of the lower bound, the performance of our algorithms is better optimized in contrast to the corresponding algorithms based on known framework incremental Euclidean restriction and incremental network expansion. More importantly, the proposed algorithms are proven to be instance optimal among classes of algorithms. Through experiments on real road network datasets, the superiority of the proposed algorithms is demonstrated.     

球面上的最近邻查询方法研究

球面上的最近邻查询在空间数据库最近邻查询领域具有重要的意义。为了处理球面上的最近邻查询问题,针对球面上数据对象点的特征和近邻查询的需要,给出了处理球面上最近邻查询的3种方法：利用球面voronoi图计算最近邻方法（VNS）;利用欧氏空间内的空间数据索引结构方法（SPINS）和降维方法（APNS）。进一步,在动态的密集数据集和动态的稀松数据集两种典型的组合情况下分别着重对3 种方法处理最近邻查询的性能进行了实验比较。理论分析和实验结果表明,给出的3种方法可较好地处理球面上具有不同性质特征的空间数据对象点的近邻查询问题。     

基于道路网的连续k近邻查询算法

针对增量式监测算法(IMA)的冗余搜索问题,提出一种基于IMA改进的移动对象连续k近邻(Continuous k Nearest Neighbor, CkNN)查询处理新算法。采用增量式查询处理机制;利用距离相近的查询其查询结果大部分相同这一特性,在以查询点为中心进行网络扩展之前,首先执行一个预处理过程,分析相近的其他查询的扩展树,并重用其中的有效部分,从而避免了对道路网的盲目扩展;且在节点的网络扩展中,通过应用具有相同扩展方向的其他查询的扩展结果,不仅减少了对道路网的重复扩展,还节省了计算代价。实验结果表明,所提算法同传统算法相比较, 缩短了查询响应时间,提高了运行效率,并且适用于不同类型的k近邻查询。     

Continuous reverse k nearest neighbor monitoring on moving objects in road networks

Continuous reverse k nearest neighbor (CRkNN) monitoring in road networks has recently received increasing attentions. However, there is still a lack of efficient CRkNN algorithms in road networks up to now. In road networks, moving query objects and data objects are restricted by the connectivity of the road network and both the object–query distance and object–object distance updates affect the result of CRkNN queries. In this paper, we present a novel algorithm for continuous and incremental evaluation of CRkNN queries in road networks. Our method is based on a novel data structure called dual layer multiway tree (DLM tree) we proposed to represent the whole monitoring region of a CRkNN query q. We propose several lemmas to reduce the monitoring region of q and the number of candidate objects as much as possible. Moreover, by associating a variable NN_count with each candidate object, we can simplify the monitoring of candidate objects. There are a large number of objects roaming in a road network and many of them are irrelevant to a specific CRkNN query of a query object q. To minimize the processing extension, for a road in the network, we give an IQL list and an IQCL list to specify the set of query objects and data objects whose location updates should be maintained for CRkNN processing of query objects. Our CRkNN method consists of two phase: the initial result generating phase and incremental maintenance phase. In each phase, algorithms with high performance are proposed to make our CRkNN method more efficient. Extensive simulation experiments are conducted and the result shows that our proposed approach is efficient and scalable in processing CRkNN queries in road networks.