K-nearest Neighbor Queries Research Articles

Efficient Shortest Path Search in Large Road Network Environment: A Heuristic Approach

The road network now opens a new application area for the classic k-nearestneighbors (k-NN) queries, which retrieve k objects closest to a given query point. However, since most existing schemes are built on top of the Euclidean distance, they just And the k objects, failing in discovering the shortest paths to them and thus possibly bringing the so-called false dismissal problem. Aiming at finding both k objects and the shortest paths at the same time, this paper first selects candidate objects by the k-NN search scheme according to the underlying index structure and then finds the path to each of them by the modified A* algorithm. The path finding step stores the intermediary paths from the query point to all of the scanned nodes and then attempts to match the path segment common between the stored paths and the path to a new scan node instead of repeatedly running A* algorithm for each k point. Experiment results show that, for the road network data of Oldenburg Road Network and California Road Network, the proposed scheme improves the search speed by 1.3–3.0 times, compared with incremental network expansion, post-Dijkstra, and naïve method, also reducing the number of scan nodes by 11.8–66.8%.

Vague continuous K-nearest neighbor queries over moving objects with uncertain velocity in road networks

Recent research has focused on Continuous K Nearest Neighbor (C KNN) queries in road networks, where the queries and the data objects are moving. Most existing approaches assume the fixed velocity of moving objects. The release of fixed moving velocity makes the query process slowly due to the significant repetitive query cost. In this paper, we study C KNN queries over moving objects with uncertain velocity in road networks. A Distance Interval Model (DIM) is designed to calculate the minimal and maximal road network distances between moving objects and query point. Furthermore, we propose a novel Possibility-based Vague KNN (PV KNN) algorithm to process the query efficiently, which determines the C KNN query results with possibility within each division time subinterval of given time interval by applying the vague set theory. In the PV KNN algorithm, the query efficiency can be improved significantly with the pruning, distilling and possibility-ranking phases. With these phases, the objects candidates are scaled down and the given time interval is divided into subintervals to reduce the repetitive query cost. In addition, an index structure TPR uv -Tree is designed to efficiently index moving objects with uncertain velocity in road network by involving edge connection and moving objects information. Experiments with simulation and comparison show that significant improvement in the performance of efficiency can be achieved with our proposed algorithms.

Monitoring continuous k-nearest neighbor queries in the hybrid wireless network

In a mobile/pervasive computing environment, one of the most important goals of monitoring continuous spatial queries is to reduce communication cost for location-updates. Existing work uses many cellular wireless connections, which would easily become the performance bottleneck of the overall system. This paper introduces a novel continuous kNN query monitoring method to reduce communication cost in the hybrid wireless network, where the moving objects in the wireless broadcasting system construct the ad-hoc network. Simulation results prove the efficiency of the proposed method, which leverages the wireless broadcasting channel as well as the WiFi link to alleviate the burden on the cellular uplink communication cost.

부분순위 연속 k 최근접 객체 탐색 기법

In the application areas of LBS(Location Based Service) and ITS(Intelligent Transportation System), continuous k-nearest neighbor query(CkNN) which is defined as a query to find the nearest points of interest to all the points on a given path is widely used. It is necessary to acquire results quickly in the above applications and be applicable to spatial network databases. It is also able to cope successfully with frequent updates of POI objects. This paper proposes a new method to search nearest POIs for moving query objects on the spatial networks. The method produces a set of split points and their corresponding k-POIs as results with partial order among k-POIs. The results obtained from experiments with real dataset show that the proposed method outperforms the existing methods. The proposed method achieves very short processing time(15%) compared with the existing method.

Scalable processing of continuous K-nearest neighbor queries with uncertain velocity

Continuous K-nearest neighbor (C KNN) query is an important type of spatio-temporal queries. Given a time interval [ t s , t e ] and a moving query object q, a C KNN query is to find the K-nearest neighbors ( KNNs) of q at each time instant within [ t s , t e ]. In this paper, we focus on the issue of scalable processing of C KNN queries over moving objects with uncertain velocity. Due to the large amount of C KNN queries that need to be evaluated concurrently, efficiently processing such queries inevitably becomes more complicated. We propose an index structure, namely the CI-tree, to predetermine and organize the candidates for each query issued by the user from anywhere and anytime. When the C KNN queries are evaluated, their corresponding candidates can be rapidly retrieved by traversing the CI-tree so that the processing time is greatly reduced. A comprehensive set of experiments is performed to demonstrate the effectiveness and the efficiency of the CI-tree.

Paged similarity queries

An important feature of a database management systems (DBMS) is its client/server architecture, where managing shared memory among the clients and the server is always an tough issue. However, similarity queries are specially sensitive to this kind of architecture, since the answer sizes vary widely. Usually, the answers of similarity query are fully processed to be sent in full to the user, who often is interested in just parts of the answer, e.g. just few elements closer or farther to the query reference. Compelling the DBMS to retrieve the full answer, further ignoring its majority is at least a waste of server processing power. Paging the answer is a technique that splits the answer onto several pages, following client requests. Despite the success of paging on traditional queries, little work has been done to support it in similarity queries. In this work, we present a technique that not only provides paging in similarity range or k-nearest neighbor queries, but also supports them in two variations: the forward similarity query and the backward similarity query. They return elements either increasingly farther of increasingly closer to the query reference. The reported experiments show that, depending on the proportion of the interesting part over the full answer, both techniques allow answering queries much faster than it is obtained in the non-paged way.

Efficient and effective similarity search over probabilistic data based on earth mover's distance

Probabilistic data is coming as a new deluge along with the technical advances on geographical tracking, multimedia processing, sensor network and RFID. While similarity search is an important functionality supporting the manipulation of probabilistic data, it raises new challenges to traditional relational database. The problem stems from the limited effectiveness of the distance metric supported by the existing database system. On the other hand, some complicated distance operators have proven their values for better distinguishing ability in the probabilistic domain. In this paper, we discuss the similarity search problem with the Earth Mover's Distance , which is the most successful distance metric on probabilistic histograms and an expensive operator with cubic complexity. We present a new database approach to answer range queries and k-nearest neighbor queries on probabilistic data, on the basis of Earth Mover's Distance. Our solution utilizes the primal-dual theory in linear programming and deploys B + tree index structures for effective candidate pruning. Extensive experiments show that our proposal dramatically improves the scalability of probabilistic databases.

Parallelizing Itinerary-Based KNN Query Processing in Wireless Sensor Networks

Wireless sensor networks have been proposed for facilitating various monitoring applications (e.g., environmental monitoring and military surveillance) over a wide geographical region. In these applications, spatial queries that collect data from wireless sensor networks play an important role. One such query is the K-Nearest Neighbor (KNN) query that facilitates collection of sensor data samples based on a given query location and the number of samples specified (i.e., K). Recently, itinerary-based KNN query processing techniques, which propagate queries and collect data along a predetermined itinerary, have been developed. Prior studies demonstrate that itinerary-based KNN query processing algorithms are able to achieve better energy efficiency than other existing algorithms developed upon tree-based network infrastructures. However, how to derive itineraries for KNN query based on different performance requirements remains a challenging problem. In this paper, we propose a Parallel Concentric-circle Itinerary-based KNN (PCIKNN) query processing technique that derives different itineraries by optimizing either query latency or energy consumption. The performance of PCIKNN is analyzed mathematically and evaluated through extensive experiments. Experimental results show that PCIKNN outperforms the state-of-the-art techniques.

Location privacy: going beyond K-anonymity, cloaking and anonymizers

With many location-based services, it is implicitly assumed that the location server receives actual users locations to respond to their spatial queries. Consequently, information customized to their locations, such as nearest points of interest can be provided. However, there is a major privacy concern over sharing such sensitive information with potentially malicious servers, jeopardizing users’ private information. The anonymity- and cloaking-based approaches proposed to address this problem cannot provide stringent privacy guarantees without incurring costly computation and communication overhead. Furthermore, they require a trusted intermediate anonymizer to protect user locations during query processing. This paper proposes a fundamental approach based on private information retrieval to process range and K-nearest neighbor queries, the prevalent queries used in many location-based services, with stronger privacy guarantees compared to those of the cloaking and anonymity approaches. We performed extensive experiments on both real-world and synthetic datasets to confirm the effectiveness of our approaches.

Query processing issues in region-based image databases

Many modern image database systems adopt a region-based paradigm, in which images are segmented into homogeneous regions in order to improve the retrieval accuracy. With respect to the case where images are dealt with as a whole, this leads to some peculiar query processing issues that have not been investigated so far in an integrated way. Thus, it is currently hard to understand how the different alternatives for implementing the region-based image retrieval model might impact on performance. In this paper, we analyze in detail such issues, in particular the type of matching between regions (either one-to-one or many-to-many). Then, we propose a novel ranking model, based on the concept of Skyline, as an alternative to the usual one based on aggregation functions and k-Nearest Neighbors queries. We also discuss how different query types can be efficiently supported. For all the considered scenarios we detail efficient index-based algorithms that are provably correct. Extensive experimental analysis shows, among other things, that: (1) the 1–1 matching type has to be preferred to the N–M one in terms of efficiency, whereas the two have comparable effectiveness, (2) indexing regions rather than images performs much better, and (3) the novel Skyline ranking model is consistently the most efficient one, even if this sometimes comes at the price of a reduced effectiveness.

Visible Reverse k-Nearest Neighbor Query Processing in Spatial Databases

Reverse nearest neighbor (RNN) queries have a broad application base such as decision support, profile-based marketing, resource allocation, etc. Previous work on RNN search does not take obstacles into consideration. In the real world, however, there are many physical obstacles (e.g., buildings) and their presence may affect the visibility between objects. In this paper, we introduce a novel variant of RNN queries, namely, visible reverse nearest neighbor (VRNN) search, which considers the impact of obstacles on the visibility of objects. Given a data set P, an obstacle set O, and a query point q in a 2D space, a VRNN query retrieves the points in P that have q as their visible nearest neighbor. We propose an efficient algorithm for VRNN query processing, assuming that P and O are indexed by R-trees. Our techniques do not require any preprocessing and employ half-plane property and visibility check to prune the search space. In addition, we extend our solution to several variations of VRNN queries, including: 1) visible reverse k-nearest neighbor (VRkNN) search, which finds the points in P that have q as one of their k visible nearest neighbors; 2) \delta-VRkNN search, which handles VRkNN retrieval with the maximum visible distance \delta constraint; and 3) constrained VRkNN (CVRkNN) search, which tackles the VRkNN query with region constraint. Extensive experiments on both real and synthetic data sets have been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms under various experimental settings.

Continuous monitoring of nearest neighbors on land surface

As geo-realistic rendering of land surfaces is becoming commonplace in geographical information systems (GIS), games and online Earth visualization platforms, a new type of k Nearest Neighbor (kNN) queries, "surface" k Nearest Neighbor (skNN) queries, has emerged and been investigated recently, which extends the traditional kNN queries to a constrained third dimension (i.e., land surface). All existing techniques, however, assume a static environment, limiting their utility in emerging applications (e.g., Location-based Services) where objects move. In this paper, for the first time, we propose two exact methods that can continuously answer skNN queries in a highly dynamic environment which allows for arbitrary movements of data objects. The first method, inspired by the existing techniques in monitoring kNN in road networks [7] maintains an analogous counterpart of the Dijkstra Expansion Tree on land surface, called Surface Expansion Tree (SE-Tree). However, we show the concept of expansion tree for land surface does not work as SE-tree suffers from intrinsic defects: it is fat and short, and hence does not improve the query efficiency. Therefore, we propose a superior approach that partitions SE-Tree into hierarchical chunks of pre-computed surface distances, called Angular Surface Index Tree (ASI-Tree). Unlike SE-tree, ASI-Tree is a well balanced thin and tall tree. With ASI-Tree, we can continuously monitor skNN queries efficiently with low CPU and I/O overheads by both speeding up the surface shortest path computations and localizing the searches. We experimentally verify the applicability and evaluate the efficiency of the proposed methods with both real world and synthetic data sets. ASI-Tree consistently and significantly outperforms SE-Tree in all cases.

Research on multi-threading processing of concurrent multiple continuous k-nearest neighbor queries

To deal with the multiple concurrent continuous k nearest neighbors queries towards moving objects,the proposed a Multi-threading Processing of Multiple Continuous Queries(MPMCQ) framework,which adopted pipeline strategy and departed the continuous query processing into three simultaneous stages: query processing,query executing and query results dispatching to improve the parallelism with multi-threading technology.Based on MPMCQ framework and grid index for moving objects,a Multi-threading processing of Continuous k Nearest Neighbor queries(MCkNN) algorithm was also presented.Experimental results and analysis show that MCkNN algorithm outperforms CPM,YPK-CNN,etc.

Algorithms for constrained k-nearest neighbor queries over moving object trajectories

An important query for spatio-temporal databases is to find nearest trajectories of moving objects. Existing work on this topic focuses on the closest trajectories in the whole data space. In this paper, we introduce and solve constrained k-nearest neighbor (CkNN) queries and historical continuous CkNN (HCCkNN) queries on R-tree-like structures storing historical information about moving object trajectories. Given a trajectory set D, a query object (point or trajectory) q, a temporal extent T, and a constrained region CR, (i) a CkNN query over trajectories retrieves from D within T, the k (≥ 1) trajectories that lie closest to q and intersect (or are enclosed by) CR; and (ii) an HCCkNN query on trajectories retrieves the constrained k nearest neighbors (CkNNs) of q at any time instance of T. We propose a suite of algorithms for processing CkNN queries and HCCkNN queries respectively, with different properties and advantages. In particular, we thoroughly investigate two types of CkNN queries, i.e., CkNNP and CkNNT, which are defined with respect to stationary query points and moving query trajectories, respectively; and two types of HCCkNN queries, namely, HCCkNNP and HCCkNNT, which are continuous counterparts of CkNNP and CkNNT, respectively. Our methods utilize an existing data-partitioning index for trajectory data (i.e., TB-tree) to achieve low I/O and CPU cost. Extensive experiments with both real and synthetic datasets demonstrate the performance of the proposed algorithms in terms of efficiency and scalability.

Efficient evaluation of continuous spatio-temporal queries on moving objects with uncertain velocity

Continuous Range (CR) query and Continuous K-Nearest Neighbor (CKNN) query are two important types of spatio-temporal queries. Given a time interval [t s , t e ] and a moving query object q, a CR query is to find the moving objects whose Euclidean distances to q are within a user-given distance at each time instant within [t s , t e ]. A CKNN query is to retrieve the K-Nearest Neighbors (KNNs) of this query object q at each time instant within [t s , t e ]. In this paper, we investigate how to process these spatio-temporal queries efficiently under the situation that the velocity of each object is not fixed. This uncertainty on the velocity of object inevitably results in high complexity in processing spatio-temporal queries. We will discuss the complications incurred by this uncertainty and propose two algorithms, namely the Possibility-based possible within objects searching algorithm and the Possibility-based possible KNN searching algorithm, for the CR query and the CKNN query, respectively. A Possibility-based model is designed accordingly to quantify the possibility of each object being the result of a CR query or a CKNN query. Comprehensive experiments are performed to demonstrate the effectiveness and the efficiency of the proposed approaches.

Search Location-Dependent Data In Broadcasting Environment

Abstract Location-based services (LBSs) provide the information based on location information as specified in a query. Queries that support LBS are called Location-Dependent Queries (LDQ). LDQ contain: range query, nearest neighbor (NN) query, k-nearest neighbor (KNN) query and reverse nearest neighbor (RNN) query. Examples of mobile LBSs include location-dependent information access (e.g., traffic reports and attractions) and nearest neighbor queries (e.g. fmding the nearest restaurant). While LDQ is well studied in the traditional wired, disk-based client-server environment, it has not yet been tackled in relation to a wireless broadcasting environrent. In this paper, the issues involved with organizing location-dependent data and answering LDQ queries on the air, aze investigated. The lineaz property of wireless broadcasting media and the power conservation requirement of mobile devices make the problem particularly interesting and challenging. An efficient data organization, called Jump Rdnn-Tree, and...

Toward the Optimal Itinerary-Based KNN Query Processing in Mobile Sensor Networks

The K-nearest neighbors (KNN) query has been of significant interest in many studies and has become one of the most important spatial queries in mobile sensor networks. Applications of KNN queries may include vehicle navigation, wildlife social discovery, and squad/platoon searching on the battlefields. Current approaches to KNN search in mobile sensor networks require a certain kind of indexing support. This index could be either a centralized spatial index or an in-network data structure that is distributed over the sensor nodes. Creation and maintenance of these index structures, to reflect the network dynamics due to sensor node mobility, may result in long query response time and low battery efficiency, thus limiting their practical use. In this paper, we propose a maintenance-free itinerary-based approach called density-aware itinerary KNN query processing (DIKNN). The DIKNN divides the search area into multiple cone-shape areas centered at the query point. It then performs a query dissemination and response collection itinerary in each of the cone-shape areas in parallel. The design of the DIKNN scheme takes into account several challenging issues such as the trade-off between degree of parallelism and network interference on query response time, and the dynamic adjustment of the search radius (in terms of number of hops) according to spatial irregularity or mobility of sensor nodes. To optimize the performance of DIKNN, a detailed analytical model is derived that automatically determines the most suitable degree of parallelism under various network conditions. This model is validated by extensive simulations. The simulation results show that DIKNN yields substantially better performance and scalability over previous work, both as kappa increases and as the sensor node mobility increases. It outperforms the second runner with up to a 50 percent saving in energy consumption and up to a 40 percent reduction in query response time, while rendering the same level of query result accuracy.

Computing Term-Concept Association in Semantic-Based Query Expansion

PDF HTML阅读 XML下载 导出引用 引用提醒 基于查询采样的高维数据混合索引 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the National Natural Science Foundation of China under Grant No.60403018 (国家自然科学基金); the National Basic Research Program of China under Grant No.2005CB321905 (国家重点基础研究发展计划(973)); the Natural Science Foundation of Shanghai of China under Grant No.04ZR14011 (上海市自然科学基金); the College Cooperation Plan of AMD (AMD大学合作计划) High Dimensional Hybrid Index Based on Query Sampling 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:为了改进高维数据库查询的效率,通常需要根据数据分布来选择合适的索引策略.然而,经典的分布模型难以解决实际应用中图像、视频等高维数据复杂的分布估计问题.提出一种基于查询采样进行数据分布估计的方法,并在此基础上提出了一种支持最近邻查询的混合索引,即针对多媒体数据分布的不均匀性,自适应地对不同分布的数据使用不同的索引结构,建立统一的索引结构.为了实现混合索引,采用构造性方法:首先通过聚类分解分割数据并建立树状索引;然后使用查询采样算法,对数据实际分布进行估计;最后根据数据分布的特性,把稀疏数据从树状索引中剪裁出来,进行基于顺序扫描策略的索引,而分布比较密集的数据仍然保留在树状索引中.在4个真实的图像数据集上进行了充分的实验,结果显示,该索引方法明显优于iDistance,M-Tree等度量空间索引,在维数达到336时,查询效率仍高于顺序扫描.实验结果显示,该查询采样算法在采样数据量仅为(N为数据量)的情况下即可获得满足索引需要的分布估计结果. Abstract:In order to improve the query answering of high-dimensional database, data distribution is necessary to select appropriate indexing strategy. However, traditional data distribution models can not estimate the accurate data distribution in the complex real multimedia data of image and video. This paper presents a method to estimate the accurate data distribution based on query sampling, and proposes a novel hybrid index to speed up processing of high-dimensional K-nearest neighbor (KNN) queries. The proposed hybrid index improves the query efficiency by adaptively selecting different index strategies for the data with different distribution. In the first step, the cluster analysis and cluster splitting methods are applied to construct a tree-based index, and then the relationship between data distribution and index performance is derived by sampling. At last some tree branches with sparse data are extracted for linear scan, while the aggregate data remains in the tree. Extensive experiments on four real image data sets show that the proposed hybrid index structure performs better than iDistance, M-Tree and linear scan, and scales better with dimensions. The index is still faster than linear scan when the dimension reaches 336. The experiments also show that the proposed query sampling algorithm can obtain the accurate data distribution when the amount of sampling is below (N is the size of data set). 参考文献 相似文献 引证文献

Group K-Nearest Neighbors queries in spatial network databases

This paper addresses the problem of Group K-Nearest Neighbors (GKNN) queries in spatial network databases, and suggests a novel approach based on real network distances. This approach essentially uses network Voronoi diagram properties together with a progressive incremental network expansion for determining the inner network distances that are needed to obtain GKNN queries.

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.