K-nearest Neighbor Queries Research Articles

Geospatial indexing for sea–land navigation based on machine learning

The exponential growth of geospatial data has created a need for efficient data processing and indexing, especially for acquiring the required data around while traveling on land and sea. Machine learning based learned index has brought a new phase in the field of spatial data indexing on land and sea for fast and accurate acquisition of points of interest. Recently proposed spatial learned indexes and traditional spatial indexes are difficult to maintain their performance while traveling on land and sea due to high memory cost and time consumption. Considering the above issue, we propose SLIP, a novel in-memory spatial learned index. SLIP uses an optimized mapping function and a piecewise linear model to handle data updates (e.g., insertion and deletion) efficiently. Based on SLIP, we design a range query algorithm and a faster KNN(K-NearestNeighbor) query algorithm using the optimized radius expansion model to get the results more quickly. Experiments on real geospatial datasets and synthetic datasets used the electronic navigation chart software show that SLIP achieved an average improvement of 10x in query performance over traditional spatial indexes and state-of-the-art learned indexes. Therefore, SLIP provides an innovative idea for high-efficiency geospatial data processing and indexing, and offers an efficient method for acquiring the surrounding geographic data for future navigation on land and sea.

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Collision detection is very important for robot motion planning. The existing accurate collision detection algorithms regard the evaluation of each node as a discrete event, ignoring the correlation between nodes, resulting in low efficiency. In this paper, we propose a novel approach that transforms collision detection into a binary classification problem. In particular, the proposed method searches the k-nearest neighbor (KNN) of the new node and estimates its collision probability by the prior node. We perform the hierarchical navigable small world (HNSW) method to query the nearest neighbor data and store the detected nodes to build the database incrementally. In addition, this research develops a KNN query technique tailored for linear data, incorporating threshold segmentation to facilitate collision detection along continuous paths. Moreover, it refines the distance function of the collision classifier to enhance the precision of probability estimations. Simulation results demonstrate the effectiveness of the proposed method.

Proximity Queries on Point Clouds using Rapid Construction Path Oracle

The prevalence of computer graphics technology boosts the developments of point clouds in recent years, which offer advantages over terrain surfaces (represented by Triangular Irregular Networks, i.e., TINs) in proximity queries, including the shortest path query, the k-Nearest Neighbor (kNN) query and the range query. Since (1) all existing on-the-fly and oracle-based shortest path query algorithms on a TIN are very expensive, (2) all existing on-the-fly shortest path query algorithms on a point cloud are still not efficient, and (3) there are no oracle-based shortest path query algorithms on a point cloud, we propose an efficient (1+ε)-approximate shortest path oracle that answers the shortest path query for a set of Points-Of-Interests (POIs) on the point cloud, which has a good performance (in terms of the oracle construction time, oracle size and shortest path query time) due to the concise information about the pairwise shortest paths between any pair of POIs stored in the oracle. Our oracle can be easily adapted to answering the shortest path query for any points on the point cloud if POIs are not given as input, and also achieve a good performance. Then, we propose efficient algorithms for answering the (1+ε)-approximate kNN and range query with the assistance of our oracle. Our experimental results show that when POIs are given (resp. not given) as input, our oracle is up to 390 times, 30 times and 6 times (resp. 500 times, 140 times and 50 times) better than the best-known oracle on a TIN in terms of the oracle construction time, oracle size and shortest path query time, respectively. Our algorithms for the other two proximity queries are both up to 100 times faster than the best-known algorithms.

Indexing dynamic encrypted database in cloud for efficient secure k-nearest neighbor query

Indexing dynamic encrypted database in cloud for efficient secure k-nearest neighbor query

The Partition Bridge (PB) tree: Efficient nearest neighbor query processing on road networks

With the rapid development of location-based services, many types of applications in urban areas such as transportation planning, traffic management, and deployment of infrastructure depend on spatial objects that are distributed along the road network. As current place datasets include millions of spatial objects and human mobility datasets capture billions of locations, it is an important challenge to answer k-nearest neighbor queries efficiently. However, shortest-path distance calculations are the computational bottleneck for a k-nearest neighbor queries on road networks. Existing query algorithms partition the network to mitigate this bottleneck, but they do not take the data distribution into account, which leads to inefficiencies in dense areas and sparse areas. In this paper, an efficient and scalable indexing method called the Partition Bridge tree (PB-tree), is proposed based on hierarchical network partitions that consider network connectivity and data distribution. The structure of the PB-tree mainly includes distance matrices, union-bridges, bridges, and active network nodes component. Based on the PB-tree, a k nearest neighbor query processing algorithm is proposed by combining “bottom-up”, “top-down”, and adjacent extension methods. By using different road networks and datasets, the effectiveness and practicability of PB-tree are evaluated; the experimental results show that PB-tree outperforms the state-of-the-art methods and the classical approach.

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

In view of the existing research in the field of k-nearest neighbor query in the road network, the incompleteness of the query user’s preference for data objects and the privacy protection of the query results are not considered, this paper proposes a multiuser incomplete preference k-nearest neighbor query algorithm based on differential privacy in the road network. The algorithm is divided into four parts; the first part proposes a multiuser incomplete preference completion algorithm based on association rules. The algorithm firstly uses the frequent pattern tree proposed in this paper to mine frequent item sets, then uses frequent item sets to mine strong correlation rules, and finally completes multiuser incomplete preference based on strong correlation rules. The second part proposes attribute preference weight coefficient based on multiuser’ s different preferences and clusters users accordingly. The third part compares the dominance of the query object, filters the data with low dominance, and performs a k-neighbor query. The fourth part proposes a privacy budget allocation method based on differential privacy technology. The method uses the Laplace mechanism to add noise to the result release and balance the privacy and availability of data. Theoretical research and experimental analysis show that the proposed method can better deal with the multiuser incomplete preference k-nearest neighbor query and privacy protection problems in the road network.

FSS-Net: A Fast Search Structure for 3D Point Clouds in Deep Learning

Article FSS-Net: A Fast Search Structure for 3D Point Clouds in Deep Learning Jiawei Wang 1, Yan Zhuang 1,*, and Yisha Liu 2,* 1 School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China 2 Information Science and Technology College, Dalian Maritime University, Dalian 116026, China * Correspondence: zhuang@dlut.edu.cn; liuyisha@dlmu.edu.cn Received: 5 April 2023 Accepted: 5 May 2023 Published: 23 June 2023 Abstract: The deep learning methods achieve good results in the semantic segmentation and classification of the 3D point clouds. The popular convolutional neural networks illustrate the importance of using the neighboring information of the points. Searching the neighboring points is an important process that can get the context information of each point. The K-nearest neighbor (KNN) search and ball query methods are usually used to find the neighboring points, but a long time is required to construct the KD-tree and calculate the Euclidean distance. In this work, we introduce a fast approach (called the voxel search) to finding the neighbors, where the key is to use the voxel coordinates to search the neighbors directly. However, it is difficult to apply this method directly to the general network structure such as the U-net. In order to improve its applicability, the corresponding up-sampling and down-sampling methods are proposed. Additionally, we propose a fast search structure network (FSS-net) which consists of the feature extraction layer and the sampling layer. In order to demonstrate the effectiveness of the FSS-net, we conduct experiments on a single object in both indoor and outdoor environments. The speed of the voxel search approach is compared with that of the KNN and ball query. The experimental results show that our method is faster and can be directly applied to any point-based deep learning networks.

Meshless power diagrams

The computation of power diagrams (or weighted Voronoi diagrams) is a fundamental task in computational geometry and computer graphics. To accomplish the computation, we provide a different way from the existing ones for lifting the weighted seeds to a set of points in the space of one dimension higher, then the power cells can be directly obtained by computing the intersections of the Voronoi cells of these lifting points and the original space. This property enables us to apply the method based on the k-nearest neighbors query to the generation of power diagrams. Each power cell is obtained by sequentially clipping the input domain using the bisectors between its seed and the k-nearest neighbors. Experimental results demonstrate that our method outperforms the state-of-the-art one based on regular triangulation in terms of efficiency for general cases in the 2D and 3D spaces.

Improving the Efficiency of the EMS-Based Smart City: A Novel Distributed Framework for Spatial Data

The smart city system, which is a type of enterprise management system (EMS), automatically manages cities and schedules resources efficiently based on spatial data generated by devices, such as the Internet of Things and mobile. However, with the increasing deployment of technologies, including sensor and location-based services, their ever-growing spatial data are no longer managed efficiently by traditional EMS. To overcome this issue, we present SeFrame, which is a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> patially <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> nabled <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">frame</u> work for improving the efficiency of smart city EMS based on a distributed architecture. The framework supports a set of spatial queries, including: The range query, k-nearest neighbors query, and spatial join query. It benefits greatly from using the buffer-enabled partition method to eliminate duplicate results. In each partition, the local index based on combination of the quad-tree and grid index (CQG) significantly improves the spatial query efficiency in memory. CQG manages complex spatial objects, including a point, polygon, and polyline. By taking full advantage of the local index, SeFrame accesses skewed spatial data in constant time. In experiments, we demonstrated that the proposed method delivered superior performance in terms of scalability and query efficiency, in most cases.

Dynamic data structures for k-nearest neighbor queries

Our aim is to develop dynamic data structures that support k-nearest neighbors (k-NN) queries for a set of n point sites in the plane in O(f(n)+k) time, where f(n) is some polylogarithmic function of n. The key component is a general query algorithm that allows us to find the k-NN spread over t substructures simultaneously, thus reducing an O(tk) term in the query time to O(k). Combining this technique with the logarithmic method allows us to turn any static k-NN data structure into a data structure supporting both efficient insertions and queries. For the fully dynamic case, this technique allows us to recover the deterministic, worst-case, O(log2⁡n/log⁡log⁡n+k) query time for the Euclidean distance claimed before, while preserving the polylogarithmic update times. We adapt this data structure to also support fully dynamic geodesic k-NN queries among a set of sites in a simple polygon. For this purpose, we design a shallow cutting based, deletion-only k-NN data structure. More generally, we obtain a dynamic planar k-NN data structure for any type of distance functions for which we can build vertical shallow cuttings. We apply all of our methods in the plane for the Euclidean distance, the geodesic distance, and general, constant-complexity, algebraic distance functions.

A PID-Based kNN Query Processing Algorithm for Spatial Data.

As a popular spatial operation, the k-Nearest Neighbors (kNN) query is widely used in various spatial application systems. How to efficiently process a kNN query on spatial big data has always been an important research topic in the field of spatial data management. The centralized solutions are not suitable for spatial big data due to their poor scalability, while the existing distributed solutions are not efficient enough to meet the high real-time requirements of some spatial applications. Therefore, we introduce the Proportional Integral Derivative (PID) control technology into kNN query processing and propose a PID-based kNN query processing algorithm (PIDKNN) for spatial big data based on Spark. In this algorithm, the whole data space is divided into grid cells of the same size using the grid partition method, and the grid-based index is constructed. On this basis, the grid-based density peak clustering algorithm is used to cluster spatial data, and the corresponding PID parameters are set for each cluster. When performing kNN queries, the PID algorithm is used to estimate the radius growth step size of kNN queries, thereby realizing kNN query processing with a variable query radius growth step based on a feedback mechanism, which greatly improves the efficiency of kNN query processing. A series of experimental results show that the PIDKNN algorithm has good performance and scalability and is superior to the existing parallel kNN query processing methods.

Modeling and Prediction of NOx Emission of a Coal-Fired Boiler by a Learning-Based KNN Mechanism

The production of nitrogen oxides (NOx) in coal-fired boiler combustion has been found as a significant source of environmental pollution. Flue gas denitrification is a standard NOx control technology for small- and medium-sized coal-fired boilers. Achieving steady-state control in flue gas denitrification can be challenging since coal-fired boiler systems have complexity and significant delay. A model based on a learning-based K-nearest neighbor (KNN) query mechanism created for NOx output soft prediction is proposed in this study. First, a knowledge base in the proposed model is established through spatial division in accordance with the previous combustion parameters. Moreover, the clusters are established based on the output NOx values. Next, the domain of values of combustion parameters for the respective cluster is obtained. Second, the optimal cluster is selected using the knowledge base for an input vector q with new combustion parameters ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]. Lastly, the K tuples in the cluster the closest to the values of the input vector q are adopted to predict the output NOx value of q. The predicted NOx value can serve as a feedforward signal to control the output of the reductant for accurate denitrification. As revealed by the experimental results, the proposed practical model, capable of conducting the prediction in a sub-second time, is highly competitive with existing techniques. Furthermore, a deep learning algorithm (DLA) is designed, whereas it underperforms the KNN model.

Visibility Detection of 3D Objects and Visual K-Nearest Neighbor Query Based on Convex Hull Model

With the rapid development of the technologies of virtual reality (VR) and augmented reality (AR), the accurate visual query of 3D objects which are closer to real world has become a hotspot of study. There are a lot of redundancy problems in the existing axis-aligned bounding box model (AABB) and oriented bounding box model (OBB), which are used to represent 3D objects mostly at present, and cannot be used for the accurate visual query. For example, the results of the visual query must be accurate, and without any error in automatic driving technology in the automotive industry, otherwise, serious safety problems would be caused. The convex hull model could be used to solve those problems. However, there are few studies on visual query based on the convex hull model. A visual query method for 3D obstacle space based on the convex hull model is proposed in this paper. Firstly, the definitions of “query point-object visual body” and “combined obstacle” are proposed. Secondly, the visibility detection algorithm of the convex hull model is given in detail. The collision detection method of “query point-object visible body” combined with other obstacles and line-plane geometric calculation operations are used to detect the visibility of the convex hull model. Then, a 3D visual k-nearest neighbor algorithm based on the convex hull model is given in the paper. Finally, the algorithm is verified by experiments and compared with the traditional visibility detection algorithm, and the analysis of experimental results shows that the algorithm has a great performance and higher accuracy. The growth rate of the query time is smaller, and the speed is faster; especially, when there are fewer query points, the query speed can be increased by more than 50%.

Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm

The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes. Because of its operation, the application of this classification may be limited to problems with a certain number of instances, particularly, when run time is a consideration. However, the classification of large amounts of data has become a fundamental task in many real-world applications. It is logical to scale the k-Nearest Neighbor method to large scale datasets. This paper proposes a new k-Nearest Neighbor classification method (KNN-CCL) which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts. The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters. The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets. Finally, sets of experiments are conducted on the UCI datasets. The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance.

Efficient computation of spatial queries over points stored in k2-tree compact data structures

We present efficient algorithms to compute two spatial queries over points stored in compact data structures. The former is the K-Nearest Neighbors Query (KNN) which given a point q gets the K-nearest points to q. The latter query is the K-Closest Pair Query (KCPQ), which obtains the K-pairs of closest neighbors between two set of points R and S on the same spatial plane. There are several efficient implementations of these queries, which work mainly with data stored in secondary memory. However, these implementations do not scale well over large datasets. Our algorithms compute the queries over large datasets of points stored in compact data structures, in main memory. Compact data structures are structures that allow efficiently storage data in main memory and query them in their compressed form. We use the k2-tree compact structure to represent points of interest. Through experimentation over synthetic and real datasets, we show that by using the k2-tree we can work with large datasets in main memory, and that the KNN and KCPQ spatial data queries can be efficiently computed over the compact data structures. We also implement a JAVA library that is available for the academic and industrial community.

Toward Oblivious Location-Based k-Nearest Neighbor Query in Smart Cities

Enabled by the flourishing Internet-of-Things technology, smart cities can offer a variety of smart services to our daily lives and have received considerable attention in recent years. As a pivotal component of smart cities, location-based services (LBSs) have been deeply exploited by both academia and industry. Meanwhile, since cloud computing can provide reliable and flexible IT resources, many LBS services have been outsourced to the cloud for offering better services. Nevertheless, as the cloud is not fully trusted, privacy preservation becomes an essential requirement for these services. Over the past years, many privacy-preserving location-based k-nearest neighbor ( kNN) query schemes over the cloud have been proposed. However, most of them are subjected to an inevitable design defect, i.e., whenever a user queries twice at the same location, the cloud can identify and return the same query result to the query user, and such information together with third-party data breaches could be exploited by the cloud for some location disclosures. Although some existing schemes can cope with the issue, they are not quite practical, as they will bring heavy overheads on the query user side. In this article, aiming to address the above challenge, we propose a novel oblivious location-based kNN query scheme, in which the cloud cannot link two queries even if they are initiated by query users at the same location. Specifically, based on the modified Paillier cryptosystem, we first present three privacy-preserving protocols, namely, oblivious absolute value calculation, sorting, and top- k extraction. Then, by integrating these three protocols, we propose our novel oblivious location-based kNN query scheme. The detailed security analysis shows that our proposed scheme really enhances the privacy preservation in LBS queries. In addition, extensive performance analysis and experiments are conducted, and the results indicate that our proposed scheme is also efficient for the query user.

Approximate k-Nearest Neighbor Query of High Dimensional Data Based on Dimension Grouping and Reducing

Approximate k-Nearest Neighbor Query of High Dimensional Data Based on Dimension Grouping and Reducing

Review on a Privacy-Preserving and Efficient kNearest Neighbor Query and Classification Scheme Based on k-Dimension Tree for Outsource Data

Review on a Privacy-Preserving and Efficient kNearest Neighbor Query and Classification Scheme Based on k-Dimension Tree for Outsource Data

Voronoi Partition to Support Data Search in Uncertain Database with k-Bound Filtering

With the development of mobile technology, one of its main functions is to have mobile navigation capabilities, this is one of the Location-Based Services (LBS). Mobile navigation is designed as a mobile device to be able to monitor access objects from users. Because of the mobile characteristic, the impact on the points of objects to be found always occur a not certain change. Efficient query processing is needed on a set of mobile data due to the movement of users. This movement has an impact on searching in an uncertain database. For this uncertain database, the important query method is Probabilistic k-Nearest Neighbor query (PkNN), which calculates the probability of the set of k objects to be closest to the given query point. Several studies have been conducted at this time in order to contribute to the search results in a mobile database and uncertain with the support of certain algorithms to produce better performance. In this study, we propose a method called voronoi partitioning to support searching in uncertain database (Partition threshold k Aggregate Nearest Neighbor query method-Partition_PANN). In designing the partition of the voronoi region, it starts by using voronoi local networks to calculate query answers for small areas around the request point. A query point that meets the threshold used as a value as a set of threshold queries. So, all of the query points that meet the thresholds are the basis for forming local network voronoi partitions. Thus, this method does not require preliminary calculations also evaluation of distances at each intersection. The purpose of this research is to improve the performance of queries in an uncertain database by making the aggregate process and trimming the probability value as one phase of the search algorithm. In the first stage, objects which not able to form the answers are filtered by calculating the minimum closed circle from the dataset of query which prepared for the trimming phase. The second step called probabilistic candidate selection, its cut significant set of candidates for inspected as the aggregate function of the nearest neighbor’s demand. The remaining set is sent for verification which obtains possible answers at the lower and upper limits so that candidates whose probabilities are not less than the user-specified threshold are saved in the result set and returned to the user. We also examine the efficient data structures spatially which support this method. Our solution can be applied to uncertain data with an ever-changing probability density function.

Trajectory-Based Spatiotemporal Entity Linking

Trajectory-based spatiotemporal entity linking is to match the same moving object in different datasets based on their movement traces. It is a fundamental step to support spatiotemporal data integration and analysis. In this paper, we study the problem of spatiotemporal entity linking using effective and concise signatures extracted from their trajectories. This linking problem is formalized as a k-nearest neighbor (k-NN) query on the signatures. Four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) are investigated for signature construction. A simple yet effective dimension reduction strategy is developed together with a novel indexing structure called the WR-tree to speed up the search. A number of optimization methods are proposed to improve the accuracy and robustness of the linking. Our extensive experiments on real-world datasets verify the superiority of our approach over the state-of-the-art solutions in terms of both accuracy and efficiency.