基于时空连续性的运动对象最近邻居查询

时空数据库中的运动对象最近邻居查询是NN Queries中的新问题，基于TPR-TREE索引结构的TP NN Queries算法能较好地处理对象的时态特性，但会多次查询同一对象。本文利用运动对象的时空连续性对TP NN Queries算法进行改进，通过一次查询TPR-TREE索引获取所有候选NN对象与查询对象的距离变化曲线，进而得到NN对象集，减少了查询及时空运算的次数。本文最后给出了实验分析。     

Reverse nearest neighbors in large graphs

A reverse nearest neighbor (RNN) query returns the data objects that have a query point as their nearest neighbor (NN). Although such queries have been studied quite extensively in Euclidean spaces, there is no previous work in the context of large graphs. In this paper, we provide a fundamental lemma, which can be used to prune the search space while traversing the graph in search for RNN. Based on it, we develop two RNN methods; an eager algorithm that attempts to prune network nodes as soon as they are visited and a lazy technique that prunes the search space when a data point is discovered. We study retrieval of an arbitrary number k of reverse nearest neighbors, investigate the benefits of materialization, cover several query types, and deal with cases where the queries and the data objects reside on nodes or edges of the graph. The proposed techniques are evaluated in various practical scenarios involving spatial maps, computer networks, and the DBLP coauthorship graph.     

基于SR-树的空间对象最近邻查询

最近邻查询是空间数据库的重要应用之一，最近邻查询概念的扩展，即时象的相似性查询中，利用以往的定位查询以厦范围查询方法不能很好的解决最近邻查询的问题，在分析NN查询的基本概念和存储区域的基础上，提出区别于以往NN查询的基于SR-树的多时象NN查询方法，根据某几个查询点，找出离它们最近的一个点或者是七个点，在某种意义上是寻求一种最优方案。     

参数自适应下基于近邻图的近似最近邻搜索

现有基于近邻图的近似最近邻搜索（ANNS）算法通常将数据库中被检索向量组织成近邻图结构,根据用户设定参数搜索查询向量的近似最近邻。为提升基于近邻图的ANNS算法在给定召回率下的搜索效率,提出一种参数自适应方法AdaptNNS。采集数据库中的被检索向量并对采样结果进行聚类,利用聚类中心向量和最近邻分类器提取查询负载特征,同时将查询负载特征与不同的召回率相结合作为输入特征训练梯度提升决策树（GBDT）模型。在查询处理过程中,根据应用程序指定的召回率获取最终输入特征,并通过GBDT模型预测最优搜索参数,提升ANNS算法的吞吐量。在Text-to-Image、DEEP和Turing-ANNS数据集上的实验结果表明,当达到相同的目标召回率时,AdaptNNS方法相比于Baseline方法最多可将DiskANN和HNSW算法的吞吐量提升1.3倍,具有更高的近似最近邻搜索效率。     

空间数据库中的线段k近邻查询研究

K近邻查询是空间数据库中的重要查询之一,k近邻查询在内容的相似性检索、模式识别、地理信息系统中有重要应用。针对现有k近邻查询都是基于点查询的情况,提出基于平面线段的k近邻查询,查找线段集中给定查询点的k个最近线段。给出基于Voronoi图的线段k近邻查询算法及给出相关定理和证明。该算法通过线段Voronoi图的邻接特性找到一个候选集,然后从中找到最终结果。通过随机数据的实验证明,所提算法明显优于线性扫描算法和基于R树的k近邻查询算法。     

利用改进NFL算法对镜头进行基于内容的检索

基于镜头的分类和检索对于视频库的管理和查询非常重要.将“最近特征线”法（nearest feature line,简称NFL）用于镜头的分类和检索.将镜头中的代表帧看做是某个特征空间中的点,通过这些点间的连线表征该镜头的总体特征信息,然后计算查询图像和特征线的距离,以决定镜头与查询图像的相似度.为了更适于视频数据,对原来的NFL方法进行了改进,基于镜头内部内容活动程度对特征线进行限制、实验结果表明,改进的NFL方法比传统的NFL方法以及常用的聚类万法,如最近邻法（nearest neighbor,简称NN）和最近中心法（nearest center,简称NC）,在性能上有所提高.     

Range nearest-neighbor query

A range nearest-neighbor (RNN) query retrieves the nearest neighbor (NN) for every point in a range. It is a natural generalization of point and continuous nearest-neighbor queries and has many applications. In this paper, we consider the ranges as (hyper)rectangles and propose efficient in-memory processing and secondary memory pruning techniques for RNN queries in both 2D and high-dimensional spaces. These techniques are generalized for kRNN queries, which return the k nearest neighbors for every point in the range. In addition, we devise an auxiliary solution-based index EXO-tree to speed up any type of NN query. EXO-tree is orthogonal to any existing NN processing algorithm and, thus, can be transparently integrated. An extensive empirical study was conducted to evaluate the CPU and I/O performance of these techniques, and the study showed that they are efficient and robust under various data sets, query ranges, numbers of nearest neighbors, dimensions, and cache sizes.     

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.     

基于簇内乘积量化的最近邻检索方法

本文针对大规模高维数据近邻检索中的瓶颈问题,提出基于向量量化的一种检索方法—簇内乘积量化树方法.该方法运用向量量化和乘积量化的多层树状结构高效表征大规模高维数据集,与现有方法相比降低了索引表空桶率;其次提出基于贪心队列的近邻簇筛选方法减小了计算复杂度,加快了近邻检索速度;最后提出面量化方法用于近似计算候选数据集向量与查询向量间的距离,与点量化和线量化方法相比量化误差更小,提高了近邻查询准确率.本文提出的簇内乘积量化树算法在算子Sift和Gist描述的大规模高维数据集上与乘积量化树技术相比,首次召回准确率提高了57.7%,索引表空桶率降低幅度在50%以上,与局部优化乘积量化技术相比,查全率高达97%,而查询时间却仅需原来的1/9.实验结果表明本文提出的基于簇内乘积量化的近邻方法提升了近邻检索性能,为大规模高维数据集近邻检索提供了理论支持.     

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.