Continuous K-nearest Neighbor Research Articles

Deep learning path-like collective variable for enhanced sampling molecular dynamics.

Several enhanced sampling techniques rely on the definition of collective variables to effectively explore free energy landscapes. The existing variables that describe the progression along a reactive pathway offer an elegant solution but face a number of limitations. In this paper, we address these challenges by introducing a new path-like collective variable called the "deep-locally non-linear-embedding," which is inspired by principles of the locally linear embedding technique and is trained on a reactive trajectory. The variable mimics the ideal reaction coordinate by automatically generating a non-linear combination of features through a differentiable generalized autoencoder that combines a neural network with a continuous k-nearest neighbor selection. Among the key advantages of this method is its capability to automatically choose the metric for searching neighbors and to learn the path from state A to state B without the need to handpick landmarks a priori. We demonstrate the effectiveness of DeepLNE by showing that the progression along the path variable closely approximates the ideal reaction coordinate in toy models, such as the Müller-Brown potential and alanine dipeptide. Then, we use it in the molecular dynamics simulations of an RNA tetraloop, where we highlight its capability to accelerate transitions and estimate the free energy of folding.

A Dynamic Grid Index for CkNN Queries on Large-Scale Road Networks with Moving Objects

As the Internet of Things devices are deployed on a large scale, location-based services are being increasingly utilized. Among these services, kNN (k-nearest neighbor) queries based on road network constraints have gained importance. This study focuses on the CkNN (continuous k-nearest neighbor) queries for non-uniformly distributed moving objects with large-scale dynamic road network constraints, where CkNN objects are continuously and periodically queried based on their motion evolution. The present CkNN high-concurrency query under the constraints of a super-large road network faces problems, such as high computational cost and low query efficiency. The aim of this study is to ensure high concurrency nearest neighbor query requests while shortening the query response time and reducing global computation costs. To address this issue, we propose the DVTG-Index (Dynamic V-Tree Double-Layer Grid Index), which intelligently adjusts the index granularity by continuously merging and splitting subgraphs as the objects move, thereby filtering unnecessary vertices. Based on DVTG-Index, we further propose the DVTG-CkNN algorithm to calculate the initial kNN query and utilize the existing results to speed up the CkNN query. Finally, extensive experiments on real road networks confirm the superior performance of our proposed method, which has significant practical applications in large-scale dynamic road network constraints with non-uniformly distributed moving objects.

A Distributed Hybrid Indexing for Continuous KNN Query Processing over Moving Objects

The magnitude of highly dynamic spatial data is expanding rapidly due to the instantaneous evolution of mobile technology, resulting in challenges for continuous queries. We propose a novel indexing approach model, namely, the Velocity SpatioTemporal indexing approach (VeST), for continuous queries, mainly Continuous K-nearest Neighbor (CKNN) and continuous range queries using Apache Spark. The proposed structure is based on a selective velocity partitioning method, i.e., since different objects have varying speeds, we divide the objects into two sets according to the actual mean speed we calculate before building the index and accessing data. Then the adopted indexing structure base unit comprises a nonoverlapping R-tree and a two dimension grid. The tree divides the space into nonoverlapping minimum bounding regions that point to the grids. Then, the uniform grid stores the object data of leaf nodes. This access method reduces the update cost and improves response time and query precision. In order to enhance performances for large-scale processing, we design a compact multilayer index structure on a distributed setting and propose a CKNN search algorithm for accurate results using a candidate cell identification process. We provide a comprehensive vision of our indexing model and the adopted query technique. The simulation results show that for query intervals of 100, the proposed approach is 13.59 times faster than the traditional approach, and the average time of the VeST approach is less than 0.005 for all query intervals. This proposed method improves response time and query precision. The precision of the VeST algorithm is almost equal to 100% regardless of the length of the query interval.

Processing Continuous k Nearest Neighbor Queries in Obstructed Space with Voronoi Diagrams

With the emergence and growing popularity of and location-based service (LBS) technologies, the continuous k nearest neighbor (CO k NN) query in obstructed space is becoming a very important service. In this article, we study the CO k NN in obstructed space, which retrieves k obstructed nearest neighbors (ONNs) for every point on a query segment q̄ . The state-of-the-art approach, called <monospace>Euclidean based CONN (E-CONN)</monospace>, exploits an R-tree to traverse the dataset P in ascending order of their Euclidean distances to q̄ . Taking a different point of view, in this article, we explore the idea of Voronoi diagram to define the notion of obstructed Voronoi diagram (OVD) . The Voronoi cells with obstacles are divided into visible and invisible regions for quickly answering nearest neighbor queries. To facilitate efficient retrieval of Voronoi cells and processing of continuous nearest neighbor (CONN) queries, we propose a new grid-based index, called Voronoi diagram with Obstacles in Grid (VO-Grid) , which indexes Voronoi cells and associated obstacle information with a grid file. Based on VO-Grid, we propose an efficient algorithm, called <monospace>CONN with VO-Grid Acceleration (CONN-VOA)</monospace>, to accelerate the CONN query processing. Moreover, we extend <monospace>CONN-VOA</monospace> to the CO k NN query, which also explores effective filtering and early termination for reducing redundant access of data objects. A comprehensive performance evaluation using both real and synthetic datasets is conducted to validate the proposed ideas and demonstrate the efficiency of our algorithms. The experimental results show that the <monospace>CONN-VOA</monospace> algorithm substantially outperforms <monospace>E-CONN</monospace> algorithm.

Direction-Aware Continuous Moving K-Nearest-Neighbor Query in Road Networks

Continuous K-nearest neighbor (CKNN) queries on moving objects retrieve the K-nearest neighbors of all points along a query trajectory. They mainly deal with the moving objects that are nearest to the moving user within a specified period of time. The existing methods of CKNN queries often recommend K objects to users based on distance, but they do not consider the moving directions of objects in a road network. Although a few CKNN query methods consider the movement directions of moving objects in Euclidean space, no efficient direction determination algorithm has been applied to CKNN queries over data streams in spatial road networks until now. In order to find the top K-nearest objects move towards the query object within a period of time, this paper presents a novel algorithm of direction-aware continuous moving K-nearest neighbor (DACKNN) queries in road networks. In this method, the objects’ azimuth information is adopted to determine the moving direction, ensuring the moving objects in the result set towards the query object. In addition, we evaluate the DACKNN query algorithm via comprehensive tests on the Los Angeles network TIGER/LINE data and compare DACKNN with other existing algorithms. The comparative test results demonstrate that our algorithm can perform the direction-aware CKNN query accurately and efficiently.

An efficient continuous k-nearest neighbor query processing scheme for multimedia data sharing and transmission in location based services

The continuous k-nearest neighbor query is one of the most important query types to share multimedia data or to continuously identify transportable users in LBS. Various methods have been proposed to efficiently process the continuous k-NN query. However, most of the existing methods suffer from high computation time and larger memory requirement because they unnecessarily access cells to find the nearest cells on a grid index. Furthermore, most methods do not consider the movement of a query. In this paper, we propose a new processing scheme to process the continuous k nearest neighbor query for efficiently support multimedia data sharing and transmission in LBS. The proposed method uses the patterns of the distance relationships among the cells in a grid index. The basic idea is to normalize the distance relationships as certain patterns. Using this approach, the proposed scheme significantly improves the overall performance of the query processing. It is shown through various experiments that our proposed method outperforms the existing methods in terms of query processing time and storage overhead.

Continuous k-Nearest Neighbor Queries in Wireless Environments

Objectives: Network Coding-based Spatial Air Index (NCSAI) has been proposed for improving query performance of continuous k-nearest neighbor queries in road networks. Methods: Due to unreliable nature of wireless links, network coding has been playing an important role in wireless environments and improves scalability and throughput. In this work,NCSAI has been adapted using XOR-based network coding because it is simple and efficient coding strategy. Findings: Experiments have been conducted for evaluating query performance, the experimental result show that performance of NCSAI better than state-of-the-art Network Partition Index (NPI). Improvements: We can improve the performance of NCSAI by adapting an efficient network coding strategies.

Probabilistic CkNN Queries of Uncertain Data in Large Road Networks

Continuous $k$ -nearest neighbor (CkNN) query processing is an important issue in spatial temporal databases. In real-world scenarios, query clients and data objects may move with uncertain speeds on the road networks, which makes retrieving the exact CkNN query result a challenge. This paper addresses the issue of processing probabilistic CkNN queries of uncertain data (CPkNN) for road networks, where moving objects and query points are restricted by the connectivity of the road network and the object-query distance updates affect the query result. A novel model is proposed to estimate network distances between moving objects and a submitted moving query in the road network. Then, a CPkNN query monitoring method is presented to continuously report the possible result objects within a given time interval. In addition, an efficient method is proposed to arrange all the candidate objects according to their probabilities of being a kNN of a query. The method then chooses the top- $k$ objects as the final query result. In addition, we extend our method to large networks with high efficiency. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed schema.

A Novel Continuous KNN Prediction Algorithm to Improve Manufacturing Policies in a VMI Supply Chain

This paper examines and compares various manufacturing policies which manufacturer may adopt so as to improve the performance of a vendor managed inventory (VMI) partnership. The goal is to maximize the combined cumulative profit of supply chain while minimizing relevant inventory management costs. The supply chain is a two-level system with a single manufacturer and single retailer at each level, in which the manufacturer takes the responsibility of overall inventories of supply chain. A base system dynamics (SD) simulation model is first employed to describe the dynamic interactions between the variables and parameters of manufacturer and retailer under VMI. Then, the mentioned policies are constructed using the base SD model that lead us to differentiate the behavior supply chain for each policies within a same duration of time. In this paper, we use continuous K-nearest neighbor (CKNN) as one of the instance-based learning methodologies to predict the best manufacturing rates. This algorithm effectively increases the combined profit of supply chain in comparison with other two policies. Accordingly, a numerical example along with a number of sensitivity analyses is conducted to evaluate the performance of mentioned policies.

Distributed Continuous k Nearest Neighbors Search over Moving Objects on Wireless Sensor Networks

Continuous k-nearest neighbor (C kNN) search is a variation of kNN search that the system persistently reports k nearest moving objects to a user. For example, system continuously returns 3 nearest moving sensors to the user. Many query processing approaches for C kNN search have been proposed in traditional environments. However, the existing client-server approaches for C kNN search are sensitive to the number of moving objects. When the moving objects quickly move, the processing load on the server will be heavy due to the overwhelming data. In this thesis, we propose a distributed C kNN search algorithm (DC kNN) on wireless sensor networks (WSNs) based on the Voronoi diagram. There are four features about DC kNN: (1) each moving object constructs a local Voronoi cell and keeps the local information; (2) in order to keep the reliability of system, the query message will be propagated to related objects; (3) using the idea of safe time, the number of updates is reduced; (4) an equation to estimate a more accurate safe time is provided. Last, we present our findings through intensive experiments and the results validate our proposed approach, DC kNN.

Continuous k nearest neighbor query algorithm based on road network

Concerning the problem of redundant search of Incremental Monitoring Algorithm(IMA),this paper proposed a new algorithm of improving Continuous k Nearest Neighbor(CkNN) queries for moving objects based on IMA.The incremental query processing mechanism was adopted.Adopting the characteristic that the close queries have similar results,a pretreatment process was first performed before the network expansion which took query point as the center,by investigating the expansion tree of other queries and reuse the effective part,thus avoiding blind expansion of road network.During the network expansion of nodes,by applying the expansion results of other queries which have the same expansion direction,not only repetitive expansion was reduced,but also computational cost was saved.The experimental results show that the query response time of proposed algorithm is reduced,the efficiency is improved compared with the traditional algorithm.In addition,the improved algorithm is applicable to different types of k nearest neighbor queries.

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.

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.

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.

Voronoi-Based Continuous $k$ Nearest Neighbor Search in Mobile Navigation

Digital ecosystems are formed by “digital organisms” in complex, dynamic, and interrelated ecosystems and utilize multiple technologies to provide cost-efficient digital services and value-creating activities. A distributed wireless mobile network that serves as the underlying infrastructure to digital ecosystems provides important applications to the digital ecosystems, two of which are mobile navigation and continuous mobile information services. Most information and query services in a mobile environment are continuous mobile query processing or continuous k nearest neighbor (CKNN), which finds the locations where interest points or interest objects change while mobile users are moving. These locations are known as “split nodes.” All of the existing works on CKNN divide the query path into segments, which is a segment of road separated by two intersections, and then, the process to find split nodes is applied to each segment. Since there are many segments (due to many intersections, obviously), processing each segment is naturally inefficient. In this paper, we propose an alternative solution to overcome this problem. We use the Voronoi diagram for CKNN [called Voronoi CKNN (VCKNN)]. Our proposed approach does not need to divide the query path into segments, hence improving the overall query processing performance. Our experiment verified the applicability of the VCKNN approach to solve CKNN queries.

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.

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.

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.