K-Aggregate Nearest Neighbor Query Method of Moving Objects in Road Networks

The range query of moving objects in a road network is widely used in battlefield environment, intelligent transportation systems, and mobile phone location. It has gradually become a research hotspot in the field of spatiotemporal data management. Most of the existing research on range query of moving objects in the road network is based on the premise of dense sampling of the device and complete storage of data. It ignores the uncertainty of discrete location data caused by a blind area of actual devices and limited memory, which cannot guarantee the accuracy of the range query. In this paper, the processing framework of probabilistic range query of moving objects in the road network is constructed. A spatiotemporal index structure is proposed based on the framework. This index can effectively represent time weights and the relationship between road sections. The probabilistic range query algorithm of moving objects in the road network with an uncertain trajectory caused by sampling frequency is designed and implemented. The experiments verify that the method proposed in this paper can improve query efficiency and ensure query accuracy.

Recently more and more people focus on continuous K-Nearest Neighbor (CKNN) query processing 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 object during a period of time. The main issue with existing methods is that moving objects change their locations frequently over time and if their location updates cannot be processed in time, the system runs the risk of retrieving the incorrect results of KNN. In this paper, an effective method is proposed to deal with continuous K-Nearest Neighbor query processing. By considering whether a moving object o is moving farther away from or getting closer to a query point q, the object which is definitely not in the KNN result set is effectively excluded. Thus we can reduce the communication cost, meanwhile we can also simplify the network distance computation between moving objects and query q. Comprehensive experiments are conducted and the results verify the effectiveness of the proposed algorithms.

With the ever-growing popularity of smartphone devices in recent years, skyline queries over spatial Web objects in road networks have received increasing attention. In the literature, various techniques have been developed to tackle skyline queries that take both spatial and non-spatial attributes into consideration. However, the existing solutions only focus on solving point-based queries, where the query location is a spatial point. We observe that in many real-life applications, the user location is often represented by a spatial range. Thus, in this paper, we study a new problem of range-based skyline queries (CRSQs) in road networks. Two efficient algorithms named landmark-based (LBA) and index-based (IBA) algorithms are proposed. We also present incremental versions of LBA and IBA to handle continuous range-based skyline queries over moving objects. Extensive experiments using real road network datasets demonstrate the effectiveness and efficiency of our proposed algorithms.

Proposed a new index structure, named MG2R*, can efficiently store and retrieve the past, present and future positions of network-constrained moving objects. It is a two-tier structure. The upper is a MultiGrid-R*-Tree (MGRT for short) that is used to index the road network. The lower is a group of independent R*-Tree. Each R*-Tree is relative to a route in the road network, can index the spatiotemporal trajectory of the moving objects in the road. Moreover, moving objects query is implemented based on this index structure. It compared to other index structures for road-network-based moving objects, such as MON-Tree, the experimental results shown that the MG2R* can effectively improve the query performance of the spatio-temporal trajectory of network-constrained moving objects.

A set of distributed continual range query requests, each defining a geographical region of interest, needs to be periodically reevaluated to provide up to date answers. Processing these continual queries efficiently and incrementally becomes important for location based services and applications. In this paper, we propose an efficient incremental method for continuous range query characterized by mobility of objects which follow paths in an underlying spatial road network. We notice that both queries and moving objects can be attached to the road link for updating of query answers. By using Grid R∗-tree which indexing spatial road network data, query requests and moving objects register themselves to corresponding road links according to query areas and moving object locations on the network. Also we analyzed the impacts to query answers caused by geometry relation of road link and query area and state of moving objects. Then we proposed an efficient update algorithm and corresponding memory data structures based on spatial and state classification which formed a safe region to avoid unnecessary updates. Finally a comprehensive experimental evaluation using real data has been conducted to demonstrate the efficiency and effectiveness of our algorithm.

Network Voronoi Diagram on uncertain objects for nearest neighbor queries

A k-Range Nearest Neighbor (or kRNN for short) query in road networks finds the k nearest neighbors of every point on the road segments within a given query region based on the network distance. The kRNN query is significantly important for location-based applications in many realistic scenarios. For example, (1) the user's location is uncertain, i.e., user's location is modeled by a spatial region, and (2) the user is not willing to reveal her exact location to preserve her privacy, i.e., her location is blurred into a spatial region. However, the existing solutions for kRNN queries simply apply the traditional k-nearest neighbor query processing algorithm multiple times, which poses a huge redundant searching overhead. To this end, we propose an efficient kRNN query processing algorithm in this paper. Our algorithm (1) employs a shared execution approach to eliminate the redundant searching overhead, and (2) provides a parameter that can be tuned to achieve a tradeoff between the query processing performance and the storage overhead, while guaranteeing the user's exact k-nearest neighbors are included in the query answers. The experimental results show that our algorithm always outperforms the existing solution in terms of query response time, and the introduced tuning parameter is an effective way to achieve the tradeoff between the query response time and the storage overhead.

Given a group $\mathcal {Q}$ of query points and a set $\mathcal {P}$ of points of interest (POIs), aggregate nearest neighbor (ANN) queries find a POI p from $\mathcal {P}$ that achieves the smallest aggregate distance. Specifically, the aggregate distance is defined over the set of distances between p and all query points in $\mathcal {Q}$ . Existing studies on ANN query mainly consider the spatial proximity, whereas the textual similarity has received considerable attention recently. In this work, we utilize user-specified query keywords to capture textual similarity. We study the aggregate keyword nearest neighbor (AKNN) queries, finding the POI that has the smallest aggregate distance and covers all query keywords. Nevertheless, existing methods on ANN query are either inapplicable or inefficient when applied to the AKNN query. To answer our query efficiently, we first develop a dual-granularity (DG) indexing schema. It preserves abstracts of the road network by a tree structure, and preserves detailed network information by an extended adjacency list. Then, we propose a minimal first search (MFS) algorithm. It traverses the tree and explores the node with the minimal aggregate distance iteratively. This method suffers from false hits arising from keyword tests. Thus, we propose the collaborative filtering technique, which performs keywords test by multiple keyword bitmaps collectively rather than by only one. Extensive experiments on both real and synthetic datasets demonstrate the superiority of our algorithms and optimizing strategies.

A collaborative approach to moving [formula omitted]-nearest neighbor queries in directed and dynamic road networks

Aggregate nearest neighbor (ANN) query returns a common interesting data object that minimizes an aggregate distance for multiple query points. In this paper, we propose a novel approach to efficiently process ANN queries in road networks. This approach includes two processes: initializing process and pruning process, which are both based on computing kNN of query points in network Voronoi diagram. Experimental results show that our approach outperforms existing approaches on both response time and page accesses.

Fast optimal aggregate point search for a merged set on road networks

Nearest neighbor (NN) queries have been extended from Euclidean spaces to road networks. Existing approaches are either based on Dijkstra-like network expansion or NN/distance precomputation. The former may cause an explosive number of node accesses for sparse datasets because all nodes closer than the NN to the query must be visited. The latter, e.g., the Voronoi Network Nearest Neighbor (VN 3) approach, can handle sparse datasets but is inappropriate for medium and dense datasets due to its high precomputation and storage overhead. In this paper, we propose a new approach that indexes the network topology based on a novel network reduction technique. It simplifies the network by replacing the graph topology with a set of interconnected tree-based structures called SPIE’s. An nd index is developed for each SPIE and our new (k)NN search algorithms on an SPIE follow a predetermined tree path to avoid costly network expansion. By mathematical analysis and experimental results, our new approach is shown to be efficient and robust for various network topologies and data distributions.KeywordsRoad NetworkNear NeighborNode AccessShort Path TreeQuery NodeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Top- k Nearest Neighbors ( k NN) problem on road network has numerous applications on location-based services. As direct search using the Dijkstra's algorithm results in a large search space, a plethora of complex-index-based approaches have been proposed to speedup the query processing. However, even with the current state-of-the-art approach, long query processing delays persist, along with significant space overhead and prohibitively long indexing time. In this paper, we depart from the complex index designs prevalent in existing literature and propose a simple index named KNN-Index. With KNN-Index, we can answer a k NN query optimally and progressively with small and size-bounded index. To improve the index construction performance, we propose a bidirectional construction algorithm which can effectively share the common computation during the construction. Theoretical analysis and experimental results on real road networks demonstrate the superiority of KNN-Index over the state-of-the-art approach in query processing performance, index size, and index construction efficiency.

In this paper, we focus on a typical situation of LBS which is to provide services for users in cars that move in a road network. For such kind of users, the problem of k-CNN search along a specific route on road network is to find out k nearest neighbor (k-NN) objects for any place on the route. k nearest neighbors are selected based on the path length from the route to the objects, and the continuous search for all the points on the route should be considered. A k-CNN search method is proposed by using an incremental k-NN search based on road network.KeywordsPath LengthRoad NetworkTarget ObjectComputation PointPriority QueueThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In order to achieve user's privacy preservation and data security of LBS server in road networks, a query scheme for points of interest(PoI) is proposed in this paper based on oblivious transfer and private information retrieval. The scheme can proceed in two phases: Firstly, we take vertexes in road networks as generating elements to construct a new data structure which is employed to store the distribution information and details of PoIs which are encrypted with different symmetric keys for sake of data security of LBS server. And an oblivious transfer protocol is presented based on the data structure, which facilitates a user to privately obtain the unique key materials corresponding to his interested PoIs without revealing any other PoI information in LBS server since the data is its asset. Then, a private information retrieval protocol is presented to obtain the interested PoI details, the user does not need to provide his location or explicit query content in the query, whereas he can derive an accurate result with the previous key materials, so it guarantees the user's privacy as well. Finally, performance analysis and experiments show that our scheme is more efficient and accurate in a real scenario compared with existing solutions.