K-nearest Neighbor Queries Research Articles

Top-n query processing in spatial databases considering bi-chromatic reverse k-nearest neighbors

A reverse k-nearest neighbor (RkNN) query retrieves the data points which regard the query point as one of their respective k nearest neighbors. A bi-chromatic reverse k-nearest neighbor (BRkNN) query is a variant of the RkNN query, considering two types of data. Given two types of data G and C, a BRkNN query regarding a data point q in G retrieves the data points from C that regard q as one of their respective k-nearest neighbors among the data points in G. Many existing approaches answer either the RkNN query or the BRkNN query. Different from these approaches, in this paper, we make the first attempt to propose a top-n query based on the concept of BRkNN queries, which ranks the data points in G and retrieves the top-n points according to the cardinalities of the corresponding BRkNN answer sets. For efficiently answering this top-n query, we construct the Voronoi Diagram of G to index the data points in G and C. From the information associated with the Voronoi Diagram of G, the upper bound of the cardinality of the BRkNN answer sets for each data point in G can be quickly computed. Moreover, based on an existing approach to answering the RkNN query and the characteristics of the Voronoi Diagram of G, we propose a method to find the candidate region regarding a BRkNN query, which tightens the corresponding search space. Finally, based on the triangle inequality, we propose an efficient refinement algorithm for finding the exact BRkNN answers from the candidate regions. To evaluate our approach on answering the top-n query, it is compared with an approach which applies a state-of-the-art algorithm for answering the BRkNN query to each data point in G. The experiment results reveal that our approach has a much better performance.

Approximate nearest neighbor algorithm based on navigable small world graphs

We propose a novel approach to solving the approximate k-nearest neighbor search problem in metric spaces. The search structure is based on a navigable small world graph with vertices corresponding to the stored elements, edges to links between them, and a variation of greedy algorithm for searching. The navigable small world is created simply by keeping old Delaunay graph approximation links produced at the start of construction. The approach is very universal, defined in terms of arbitrary metric spaces and at the same time it is very simple. The algorithm handles insertions in the same way as queries: by finding approximate neighbors for the inserted element and connecting it to them. Both search and insertion can be done in parallel requiring only local information from the structure. The structure can be made distributed. The accuracy of the probabilistic k-nearest neighbor queries can be adjusted without rebuilding the structure.The performed simulation for data in the Euclidean spaces shows that the structure built using the proposed algorithm has small world navigation properties with log2(n) insertion and search complexity at fixed accuracy, and performs well at high dimensionality. Simulation on a CoPHiR dataset revealed its high efficiency in case of large datasets (more than an order of magnitude less metric computations at fixed recall) compared to permutation indexes. Only 0.03% of the 10 million 208-dimensional vector dataset is needed to be evaluated to achieve 0.999 recall (virtually exact search). For recall 0.93 processing speed 2800queries/s can be achieved on a dual Intel X5675 Xenon server node with Java implementation.

Efficiently Monitoring Reverse k-Nearest Neighbors in Spatial Networks

Given a set of clients C , a set of facilities F and a query q ∈ F , a reverse k-nearest neighbor (RkNN) query retrieves every client c ∈ C for which q is one of the k closest facilities. In the past few years, RkNN queries have received significant research attention due to their wide range of applications. In this paper, we study the problem of continuous monitoring of RkNN queries in road networks. The state-of-the-art technique is sensitive toward the movement of clients, e.g. whenever a client that is inside the so-called unpruned region changes its location, the existing technique requires expensive verification of whether the client is an RkNN of q or not. To address this problem, we utilize the novel concept of influence zone, which is a region in the network such that a client c is the RkNN if and only if it lies inside this zone. This significantly improves performance because the problem of continuously monitoring RkNN queries is reduced to the problem of continuously monitoring the clients that are inside this zone. Based on several non-trivial observations, we present an efficient algorithm to compute the influence zone. Our extensive experimental study demonstrates that our algorithm is more than an order of magnitude faster than the state-of-the-art algorithm.

Continuous K-Nearest Neighbor Processing Based on Speed and Direction of Moving Objects in a Road Network.

Recent research has focused on Continuous K-Nearest Neighbor (CKNN) query over moving objects in road networks. A CKNN query is to find among all moving objects the K-Nearest Neighbors (KNNs) of a moving query point within a given time interval. As the data objects move frequently and arbitrarily in road networks, the frequent updates of object locations make it complicated to process CKNN accurately and efficiently. In this paper, according to the relative moving situation between the moving objects and the query point, a Moving State of Object (MSO) model is presented to indicate the relative moving state of the object to the query point. With the help of this model, we propose a novel Object Candidate Processing (OCP) algorithm to highly reduce the repetitive query cost with pruning phase and refining phase. In the pruning phase, the data objects which cannot be the KNN query results are excluded within the given time interval. In the refining phase, the time subintervals of the given time interval are determined where the certain KNN query results are obtained. Comprehensive experiments are conducted and the results verify the effectiveness of the proposed methods.

Approximate similarity search for online multimedia services on distributed CPU–GPU platforms

Similarity search in high-dimensional spaces is a pivotal operation for several database applications, including online content-based multimedia services. With the increasing popularity of multimedia applications, these services are facing new challenges regarding (1) the very large and growing volumes of data to be indexed/searched and (2) the necessity of reducing the response times as observed by end-users. In addition, the nature of the interactions between users and online services creates fluctuating query request rates throughout execution, which requires a similarity search engine to adapt to better use the computation platform and minimize response times. In this work, we address these challenges with Hypercurves, a flexible framework for answering approximate k-nearest neighbor (kNN) queries for very large multimedia databases. Hypercurves executes in hybrid CPU---GPU environments and is able to attain massive query-processing rates through the cooperative use of these devices. Hypercurves also changes its CPU---GPU task partitioning dynamically according to the observed load, aiming for optimal response times. In our empirical evaluation, dynamic task partitioning reduced query response times by approximately 50 % compared to the best static task partition. Due to a probabilistic proof of equivalence to the sequential kNN algorithm, the CPU---GPU execution of Hypercurves in distributed (multi-node) environments can be aggressively optimized, attaining superlinear scalability while still guaranteeing, with high probability, results at least as good as those from the sequential algorithm.

K-Nearest Neighbor Query Processing Algorithms for a Query Region in Road Networks

Recent development of wireless communication technologies and the popularity of smart phones are making location-based services (LBS) popular. However, requesting queries to LBS servers with users’ exact locations may threat the privacy of users. Therefore, there have been many researches on generating a cloaked query region for user privacy protection. Consequently, an efficient query processing algorithm for a query region is required. So, in this paper, we propose k-nearest neighbor query (k-NN) processing algorithms for a query region in road networks. To efficiently retrieve k-NN points of interest (POIs), we make use of the Island index. We also propose a method that generates an adaptive Island index to improve the query processing performance and storage usage. Finally, we show by our performance analysis that our k-NN query processing algorithms outperform the existing k-Range Nearest Neighbor (kRNN) algorithm in terms of network expansion cost and query processing time.

Finding influential location regions based on reverse k-neighbor queries

In this paper we introduce and solve several problems that arise in the single facility location field. A reverse k-influential location problem finds a region such that the location of a new facility, desirable or obnoxious, in the region guarantees a minimum k-influential value associated to the importance, attractiveness or repulsiveness, of the facility as a solution to a reverse k-nearest or farthest neighbor query. Solving reverse k-influential location problems help decision makers to progress towards suitable locations for a new facility. We present a parallel approach, to be ran on a graphics processing unit, for approximately solving reverse k-influential location problems, and also provide and discuss experimental results showing the efficiency and scalability of our approach.

L-overlay: A layered data management scheme for peer-to-peer computing

Efficient storage and handling of data stored in a peer-to-peer (P2P) network, proves vital for various applications such as query processing and data mining. This paper presents a distributed, scalable and robust layered overlay (L-overlay) to index and manage multidimensional data in a dynamic P2P network. The proposed method distinguishes between the data and peer layers, with efficient mapping between the two. The data is organized such that semantically similar data objects are accessed hastily. Grid and tree structures are proposed for the peer layer. As application examples of L-overlay in query processing and data mining, k-nearest neighbors query processing and distributed Naive Bayes classification algorithms, are proposed. We show the effectiveness of our scheme in static and dynamic environments using simulation. L-overlay is shown to be more efficient than SSW, an available semantic overly, in terms of maintenance and query processing costs.

Efficient Maximum Range Search on Remote Spatial Databases Using k-Nearest Neighbor Queries

Supporting aggregate range queries on remote spatial databases suffers from 1) huge and/or large numbers of databases, and 2) limited type of access interfaces. This paper applies the Regular Polygon based Search Algorithm (RPSA) to effectively addressing these problems. This algorithm requests a series of k-NN queries to obtain approximate aggregate range query results. The query point of a subsequent k-NN query is chosen from among the vertices of a regular polygon inscribed in a previously searched circle. Experimental results for maximum range query searches show that Precision is over 0.87 for a uniformly distributed dataset,over 0.92 for a skew-distributed dataset,and over 0.90 for a real dataset. Also, Number of Requests (NOR) ranges between 3.2 and 4.3, between 3.9 and 4.9, and between 3.0 and 4.2, respectively.

Approximate search algorithm for aggregate k-nearest neighbour queries on remote spatial databases

Searching Aggregate k-Nearest Neighbour k-ANN queries on remote spatial databases suffers from a large amount of communication. In order to overcome the difficulty, RQP-M algorithm for efficiently searching k-ANN query results is proposed in this paper. It refines query results originally searched by RQP-S with subsequent k-NN queries, whose query points are chosen among vertices of a regular polygon inscribed in a circle searched previously. Experimental results show that precision of sum k-NN query results is over 0.95 and Number of Requests NOR is at most 4.0. On the other hand, precision of max k-NN query results is over 0.95 and NOR is at most 5.6. RQP-M brings 0.04-0.20 increase in PRECISION of sum k-NN query results and over 0.40 increase in that of max k-NN query results, respectively, in comparison with RQP-S.

Continuous range k-nearest neighbor queries in vehicular ad hoc networks

A driver should constantly keep an eye on nearby vehicles in order to avoid collisions. Unfortunately, the driver often does not see nearby vehicles because of obstacles (e.g., other vehicles, trees, buildings, etc.). This paper introduces a novel type of query, called a continuous range k-nearest neighbor (CRNN) query, in vehicular ad hoc networks, and it presents a new approach to process such a query. Most existing solutions to continuous nearest neighbor (CNN) queries focus on static objects, such as gas stations and restaurants, while this work concentrates on CRNN queries over moving vehicles. This is a challenging problem due to the high mobility of the vehicles. The CRNN query has characteristics in common with continuous range (CR) and CNN queries. In terms of CNN queries, the proposed approach achieves the same goal as the existing solutions, which is to decide effectively on valid intervals during which the query result remains unchanged. The proposed scheme aims to minimize the use of wireless network bandwidth, the computational cost, and the local storage while preserving information on the continuous movement of vehicles within the broadcast range of a given vehicle. Extensive experimental results confirm the effectiveness and superiority of the proposed scheme in comparison with an existing method.

Spatial inverse query processing

Traditional spatial queries return, for a given query object q, all database objects that satisfy a given predicate, such as epsilon range and k-nearest neighbors. This paper defines and studies inverse spatial queries, which, given a subset of database objects Q and a query predicate, return all objects which, if used as query objects with the predicate, contain Q in their result. We first show a straightforward solution for answering inverse spatial queries for any query predicate. Then, we propose a filter-and-refinement framework that can be used to improve efficiency. We show how to apply this framework on a variety of inverse queries, using appropriate space pruning strategies. In particular, we propose solutions for inverse epsilon range queries, inverse k-nearest neighbor queries, and inverse skyline queries. Furthermore, we show how to relax the definition of inverse queries in order to ensure non-empty result sets. Our experiments show that our framework is significantly more efficient than naive approaches.

도로 네트워크 환경에서 암호화된 공간데이터를 위한 K-최근접점 질의 처리 알고리즘

Due to the recent advancement of cloud computing, the research on database outsourcing has been actively done. Moreover, the number of users who utilize Location-based Services(LBS) has been increasing with the development in w ireless communication technology and mobile devices. Therefore, LBS providers attempt to outsource their spatial database to service provider, in order to reduce costs for data storage and management. However, because unauthorized access to sensitive data is possible in spatial database outsourcing, it is necessary to study on the preservation of a user's privacy. Thus, we, in this paper, propose a spatial data encryption scheme to produce outsourced database from an original database. We also propose a k-Nearest Neighbor(k-NN) query processing algorithm that efficiently performs k-NN by using the outsourced database. Finally, we show from performance analysis that our algorithm outperforms the existing one.

K-Nearest neighbor query processing algorithm for cloaking regions towards user privacy protection in location-based services

Due to the advancement of wireless internet and mobile positioning technology, the application of location-based services (LBSs) has become popular for mobile users. Since users have to send their exact locations to obtain the service, it may lead to several privacy threats. To solve this problem, a cloaking method has been proposed to blur users' exact locations into a cloaked spatial region with a required privacy threshold (k). With the cloaked region, an LBS server can carry out a k-nearest neighbor (k-NN) search algorithm. Some recent studies have proposed methods to search k-nearest POIs while protecting a user's privacy. However, they have at least one major problem, such as inefficiency on query processing or low precision of retrieved result. To resolve these problems, in this paper, we propose a novel k-NN query processing algorithm for a cloaking region to satisfy both requirements of fast query processing time and high precision of the retrieved result. To achieve fast query processing time, we propose a new pruning technique based on a 2D-coodinate scheme. In addition, we make use of a Voronoi diagram for retrieving the nearest POIs efficiently. To satisfy the requirement of high precision of the retrieved result, we guarantee that our k-NN query processing algorithm always contains the exact set of k nearest neighbors. Our performance analysis shows that our algorithm achieves better performance in terms of query processing time and the number of candidate POIs compared with other algorithms.

RFID Probable <I>k</I>-NN Query Techniques in the Semi-Constraint Space

PDF HTML阅读 XML下载 导出引用 引用提醒 半限制空间内的RFID 可能性k-近邻查询技术 DOI: 10.3724/SP.J.1001.2012.04060 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(61003058, 60933001); 国家重点基础研究发展计划(973)(2012CB316201); 中央高校基本科研业务费专项资金(N110404006) RFID Probable k-NN Query Techniques in the Semi-Constraint Space Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:作为一种监控与跟踪车流和人类活动等的潜在技术,RFID(radio frequency identification)已经在数据库领域得到了很大关注.RFID 监控对象上的k-近邻查询是一种最重要的时空查询,能够用来支持有价值的高层信息分析.但是,不同于没有限制的空间和基于限制的空间,RFID 监控场景通常被设置在一种半限制的空间内,需要新的存储和距离计算策略.此外,监控对象位置的不确定性对查询语义和处理方法提出了挑战.提出了半限制空间的概念, 并且分析了基于RFID 的半限制空间的模型.基于半限制空间,在给定一个动态查询点的基础上,提出了3 种模型和算法以有效地估计可能性k-近邻的查询结果,并采用一些特殊的索引技术加快查询的速度.实验对提出算法的效率和准确性进行了评估,表明了相关方法的有效性. Abstract:As a promising technology for monitoring and tracing the vehicle flows and human activities, radio frequency identification (RFID) has received much attention in database community. k-nearest neighbor (k-NN) query over RFID monitored objects is one of the most important spatio-temporal queries used to support valuable information analysis. Different from the constraint-free space and constraint-based space, however, RFID monitoring scenario is usually merged into a semi-constraint space, which desires new data storage and distance evaluation strategies. Furthermore, the uncertainty of the monitored object locations challenges the query semantics and processing methods. In this paper, the concept of semi-constraint space is proposed, and the RFID-based semi-constraint space model is analyzed. Based on the semi-constraint space, three models and algorithms are proposed to estimate the probable k-NN results given a dynamic query point. Some special indexing techniques are adopted to speed the query. The experiment evaluates the efficiency and accuracy of the proposed algorithms and proves the effectiveness of relative methods. 参考文献 相似文献 引证文献

무선방송환경에서 계층적 비트맵 기반 공간 색인을 이용한 k-최근접 질의처리

최근 무선방송 환경을 기반으로 하는 k-최근접(k-Nearest Neighbor) 질의처리가 활발히 연구되고 있다. 무선방송환경의 장점은 서버 내에 존재하는 불특정 다수에게 일괄적으로 질의처리를 할 수 있는 확장성을 가진다는 것이다. 그러나 기존의 k-NN 질의는 무선방송환경에 적용할 경우 탐색과정에서 백트래킹이 발생하여 질의처리시간이 증가하는 단점을 가진다. 본 논문은 무선방송환경에서 k-NN 질의를 효과적으로 처리하기 위하여 계층적 비트맵 기반 공간색인(Hierarchical Bitmap-based Spatial Index: HBI)을 제안한다. HBI는 비트맵 정보와 트리 구조를 이용하여 비트맵의 크기를 줄인다. 결과적으로 방송주기를 줄임으로써 클라이언트의 청취시간과 질의처리 시간을 줄일 수 있다. 또한 비트맵 정보를 활용하여 객체의 위치를 모두 파악할 수 있기 때문에 필요한 데이터를 선택적으로 청취할 수 있다. 본 논문에서는 HBI를 k-NN 질의에 적용하여 실험을 실시하고 성능평가에서 제안 기법이 우수함을 증명한다. Recently, k-nearest neighbors query methods based on wireless broadcasting environment are actively studied. The advantage of wireless broadcasting environment is the scalability that enables collective query processing for unspecified users connected to the server. However, in case existing k-NN query is applied in wireless broadcasting environment, there can be a disadvantage that backtracking may occur and consequently the query processing time is increasing. In this paper proposes a hierarchical bitmap-based spatial index in order to efficiently process the k-NN queries in wireless broadcasting environment. HBI reduces the bitmap size using such bitmap information and tree structure. As a result, reducing the broadcast cycle can reduce the client's tuning time and query processing time. In addition, since the locations of all the objects can be detected using bitmap information, it is possible to tune to necessary data selectively. For this paper, a test was conducted implementing HBI to k-NN query and the proposed technique was proved to be excellent by a performance evaluation.

A Distributed Approach to Continuous Monitoring of Constrained k-Nearest Neighbor Queries in Road Networks

Given two positive parameters k and r, a constrained k-nearest neighbor (CkNN) query returns the k closest objects within a network distance r of the query location in road networks. In terms of the scalability of monitoring these CkNN queries, existing solutions based on central processing at a server suffer from a sudden and sharp rise in server load as well as messaging cost as the number of queries increases. In this paper, we propose a distributed and scalable scheme called DAEMON for the continuous monitoring of CkNN queries in road networks. Our query processing is distributed among clients (query objects) and server. Specifically, the server evaluates CkNN queries issued at intersections of road segments, retrieves the objects on the road segments between neighboring intersections, and sends responses to the query objects. Finally, each client makes its own query result using this server response. As a result, our distributed scheme achieves close-to-optimal communication costs and scales well to large numbers of monitoring queries. Exhaustive experimental results demonstrate that our scheme substantially outperforms its competitor in terms of query processing time and messaging cost.

A Distributed Approach to Continuous Monitoring of Constrained k-Nearest Neighbor Queries in Road Networks

Given two positive parameters k and r, a constrained k-nearest neighbor CkNN query returns the k closest objects within a network distance r of the query location in road networks. In terms of the scalability of monitoring these CkNN queries, existing solutions based on central processing at a server suffer from a sudden and sharp rise in server load as well as messaging cost as the number of queries increases. In this paper, we propose a distributed and scalable scheme called DAEMON for the continuous monitoring of CkNN queries in road networks. Our query processing is distributed among clients query objects and server. Specifically, the server evaluates CkNN queries issued at intersections of road segments, retrieves the objects on the road segments between neighboring intersections, and sends responses to the query objects. Finally, each client makes its own query result using this server response. As a result, our distributed scheme achieves close-to-optimal communication costs and scales well to large numbers of monitoring queries. Exhaustive experimental results demonstrate that our scheme substantially outperforms its competitor in terms of query processing time and messaging cost.

Processing generalized k-nearest neighbor queries on a wireless broadcast stream

In this paper, we investigate the problem of processing generalized k-nearest neighbor (G kNN) queries, which involve both spatial and non-spatial specifications for data objects, in a wireless broadcasting system. We present a method for processing G kNN queries on the broadcast stream. In particular, we propose a novel R-tree variant index structure, called the bit-vector R-tree (bR-tree), which stores additional bit-vector information to describe non-spatial attribute values of the data objects. In addition, each node in the bR-tree stores only one pointer to its children, which makes the bR-tree compact. We generate the broadcast stream by multiplexing the bR-tree and the data objects in the broadcasting channel. The corresponding search algorithm for the broadcast stream is also described. Through a series of comprehensive simulation experiments, we prove the efficiency of the proposed method with regard to energy consumption, latency, and memory requirement, which are the major performance concerns in a wireless broadcasting system. Furthermore, we test the practicality of the proposed method in a real prototype system.

SPY-TEC+ : AN INTEGRATED INDEX STRUCTURE FOR k-NEAREST NEIGHBOR QUERIES WITH SEMANTIC PREDICATES IN MULTIMEDIA DATABASE

Recently, advanced multimedia applications, such as geographic information system, and content-based multimedia retrieval system, require the efficient processing of k-nearest neighbor queries over large collection of multimedia objects. These queries usually include the semantic information that is represented by text, as well as the visual information that is represented by a high-dimensional feature vector. Among the available techniques for processing such queries, the incremental nearest neighbor algorithm proposed by Hjaltason and Samet is known as the best choice. However, the R-tree used in their algorithm has no facility capable of partially pruning the candidate tuples that will turn out not to satisfy the semantic predicate. Also, the R-tree does not perform sufficiently well on high-dimensional data even though it provides good results on low or middle-dimensional data. These drawbacks may lead to a poor performance when processing the query. In this paper, we propose an integrated index structure, so-called SPY-TEC+, that provides an efficient method for indexing the visual and semantic feature at the same time using the SPY-TEC that was proposed for indexing high-dimensional data, and the signature file. We also propose an efficient incremental nearest neighbor algorithm for processing k-nearest neighbor queries with visual and semantic predicates on the SPY-TEC+. Finally, we show that the SPY-TEC+ enhances the performance of the SPY-TEC for processing k-nearest neighbor queries with visual and semantic predicates through various experiments.