Queries In Road Networks Research Articles

Top-k Spatial Preference Queries in Directed Road Networks

Top-k spatial preference queries rank objects based on the score of feature objects in their spatial neighborhood. Top-k preference queries are crucial for a wide range of location based services such as hotel browsing and apartment searching. In recent years, a lot of research has been conducted on processing of top-k spatial preference queries in Euclidean space. While few algorithms study top-k preference queries in road networks, they all focus on undirected road networks. In this paper, we investigate the problem of processing the top-k spatial preference queries in directed road networks where each road segment has a particular orientation. Computation of data object scores requires examining the scores of each feature object in its spatial neighborhood. This may cause the computational delay, thus resulting in a high query processing time. In this paper, we address this problem by proposing a pruning and grouping of feature objects to reduce the number of feature objects. Furthermore, we present an efficient algorithm called TOPS that can process top-k spatial preference queries in directed road networks. Experimental results indicate that our algorithm significantly reduces the query processing time compared to period solution for a wide range of problem settings.

A Spatial Mashup Service for Efficient Evaluation of Concurrent -NN Queries

Although the travel time is the most important information in road networks, many spatial queries, e.g., $k$ -nearest-neighbor ( $k$ -NN) and range queries, for location-based services (LBS) are only based on the network distance. This is because it is costly for an LBS provider to collect real-time traffic data from vehicles or roadside sensors to compute the travel time between two locations. With the advance of web mapping services, e.g., Google Maps, Microsoft Bing Maps, and MapQuest Maps, there is an invaluable opportunity for using such services for processing spatial queries based on the travel time. In this paper, we propose a server-side S patial M ashup S ervice (SMS) that enables the LBS provider to efficiently evaluate $k$ -NN queries in road networks using the route information and travel time retrieved from an external web mapping service. Due to the high cost of retrieving such external information, the usage limits of web mapping services, and the large number of spatial queries, we optimize the SMS for a large number of $k$ -NN queries. We first discuss how the SMS processes a single $k$ -NN query using two optimizations, namely, direction sharing and parallel requesting . Then, we extend them to process multiple concurrent $k$ -NN queries and design a performance tuning tool to provide a trade-off between the query response time and the number of external requests and more importantly, to prevent a starvation problem in the parallel requesting optimization for concurrent queries. We evaluate the performance of the proposed SMS using MapQuest Maps, a real road network, real and synthetic data sets. Experimental results show the efficiency and scalability of our optimizations designed for the SMS.

Optimal Location Queries in Road Networks

In this article, we study an optimal location query based on a road network. Specifically, given a road network containing clients and servers, an optimal location query finds a location on the road network such that when a new server is set up at this location, a certain cost function computed based on the clients and servers (including the new server) is optimized. Two types of cost functions, namely, MinMax and MaxSum, have been used for this query. The optimal location query problem with MinMax as the cost function is called the MinMax query, which finds a location for setting up a new server such that the maximum cost of a client being served by his/her closest server is minimized. The optimal location query problem with MaxSum as the cost function is called the MaxSum query, which finds a location for setting up a new server such that the sum of the weights of clients attracted by the new server is maximized. The MinMax query and the MaxSum query correspond to two types of optimal location query with the objectives defined from the clients' perspective and from the new server's perspective, respectively. Unfortunately, the existing solutions for the optimal query problem are not efficient. In this article, we propose an efficient algorithm, namely, MinMax-Alg ( MaxSum-Alg ), for the MinMax (MaxSum) query, which is based on a novel idea of nearest location component . We also discuss two extensions of the optimal location query, namely, the optimal multiple-location query and the optimal location query on a 3D road network. Extensive experiments were conducted, showing that our algorithms are faster than the state of the art by at least an order of magnitude on large real benchmark datasets. For example, in our largest real datasets, the state of the art ran for more than 10 (12) hours while our algorithm ran within 3 (2) minutes only for the MinMax (MaxSum) query, that is, our algorithm ran at least 200 (600) times faster than the state of the art.

On Efficient Aggregate Nearest Neighbor Query Processing in Road Networks

An aggregate nearest neighbor (ANN) query returns a point of interest (POI) that minimizes an aggregate function for multiple query points. In this paper, we propose an efficient approach to tackle ANN queries in road networks. Our approach consists of two phases: searching phase and pruning phase. In particular, we first continuously compute the nearest neighbors (NNs) for each query point in some specific order to obtain the candidate POIs until all query points find a common POI. Second, we filter out the unqualified POIs based on the pruning strategy for a given aggregate function. The two-phase process is repeated until there remains only one candidate POI, and the remained one is returned as the final result. In addition, we discuss the partition strategies for query points and the approximate ANN query for the case where the number of query points is huge. Extensive experiments using real datasets demonstrate that our proposed approach outperforms its competitors significantly in most cases.

A fast privacy-preserving framework for continuous location-based queries in road networks

The prevalence of location based services (LBS) gives rise to personal privacy concerns as users share their locations and queries to obtain desired services. For continuous queries where users report their locations periodically, attackers can infer more about users׳ privacy by analyzing the correlations of their snapshot samples. Traditional privacy-preserving solutions designed in Euclidean space can be hardly applied to the road network environment because of their ignorance of network topological properties. In this paper, we propose a novel continuous query privacy-preserving framework in road networks. Our framework is based on the concepts of k-anonymity and l-diversity. To achieve the quality of service, the distance limitation is taken into account. We build an Snet hierarchy based on the density of users, history traces, and road network topologies to accelerate the cloaking process performed at the anonymization server. Two types of cloaking algorithms, for a single user and a batch of users, are designed. The security analysis shows that our framework is robust to typical attacks. We evaluate our framework from the aspects of privacy-preserving ability, quality of service, and system performance, which indicates that our framework can provide good privacy protection while ensuring users׳ quality of service.

Continuous Skyline Queries for Moving Objects in Road Networks

Skyline queries in road networks have received much attention because of their wide applications in LBS. Due to the increasing demands for services, skyline queries for moving objects in road networks which involve not only static attributes, but also dynamic ones, become more important. This paper studies a problem of how to process the continuous skyline query for moving objects in road networks. We propose an event-based incremental processing algorithm on the strength of tracking dominant relationships between objects with the distances changing and finding the result changing points. Extensive experiments are conducted to evaluate the performance of our proposed algorithm.

Network Voronoi Diagram on uncertain objects for nearest neighbor queries

In the past decade, probabilistic nearest neighbor (PNN) query processing has received significant research attention due to the development of mobile smart terminals and the advances in wireless communication technologies. However, to the best of our knowledge, most of the existing PNN-oriented studies are aimed at the Euclidean space and cannot be readily extended to road networks. This paper takes the first step toward processing PNN queries in road Networks (NPNN). We first present an efficient method to construct Network Voronoi Diagram on Uncertain objects (UNVD), in which we first find the possible NNs of all the vertices, and then compute the u-edges as well as their corresponding possible NNs. Next, to process NPNN queries, we first present a computational method to calculate the probabilities for each possible nearest object, and then propose two data structures, namely gIndex and qIndex, to index the u-edges in UNVD. Finally, we evaluate the performance of our NPNN query processing methods via extensive experiments on both real road networks and synthetic 2-dimensional grid networks. Experimental results demonstrate the effectiveness and efficiency of our methods in terms of I/O cost and query time.

A privacy-aware monitoring algorithm for moving $$k$$ k -nearest neighbor queries in road networks

Location privacy is a major obstacle in the ubiquitous deployment of mobile and pervasive computing services. In this study, we present a new approach for preserving the trajectory privacy of moving $$k$$k-nearest neighbor (M$$k$$kNN) queries in road networks. Several location anonymization algorithms have been proposed for providing location privacy to users traveling on a road network. These algorithms focus primarily on the location anonymization of snapshot queries. Indeed, users move freely and arbitrarily, and thus query results provided to them soon become invalid as their locations change. To refresh the query result, each user must therefore periodically contact the location-based service, enabling attackers to identify and track the user easily. In addition, frequent location updates for the user may incur severe computational and communication costs. We address these issues by proposing a privacy-aware monitoring algorithm, called PAMA, for preserving the trajectory privacy of M$$k$$kNN queries in road networks. Our simulation results show that PAMA significantly outperforms conventional algorithms in terms of both security and performance.

Efficient processing of continuous min–max distance bounded query with updates in road networks

In the last decade, the research community focuses on the design of various methods in support of spatio-temporal queries in road networks. In this paper, we present a novel and important query, named the continuous min–max distance bounded query (CM2DBQ). Given a moving query object q, a minimal distance dm, and a maximal distance dM, a CM2DBQ retrieves the bounded objects whose road distances to q are within the range [dm,dM] at each time instant. We propose two algorithms, named the Continuous Within Query-based (CWQ-based) algorithm and the CM2DBQalgorithm, to efficiently determine the bounded objects. In addition, we design a mechanism, named the bounded objects updating mechanism, to rapidly evaluate the new query result when object updates occur. Extensive experiments using real road network dataset demonstrate the efficiency of the proposed algorithms and the usefulness of the bounded objects updating mechanism.

Searching continuous nearest neighbors in road networks on the air

Recently, people have begun to deal with location-based queries (LBQs) under broadcast environments. To the best of our knowledge, most of the existing broadcast-based LBQ methods are aimed at Euclidean spaces and cannot be readily extended to road networks. This paper takes the first step toward processing Continuous Nearest Neighbor queries in road Networks under wireless Broadcast environments (CN3B). Our method leverages the key properties of Network Voronoi Diagram (NVD). We first present an efficient method to partition the NVD structure of the underlying road networks into a set of grid cells and number the grid cells obtained, based on which we further propose an NVD-based Distributed air Index (NVD-DI) to support CN3B query processing. Finally, we propose an efficient algorithm on the client side to process CN3B queries. Extensive simulation experiments have been conducted to demonstrate the efficiency of our approach. The results show that our proposed method is about 4 and 7.6 times more efficient than a less-sophisticated D-tree air index based method, in access latency and tuning time, respectively.

Dynamic monitoring of optimal locations in road network databases

Optimal location (OL) queries are a type of spatial queries that are particularly useful for the strategic planning of resources. Given a set of existing facilities and a set of clients, an OL query asks for a location to build a new facility that optimizes a certain cost metric (defined based on the distances between the clients and the facilities). Several techniques have been proposed to address OL queries, assuming that all clients and facilities reside in an $$L_p$$ L p space. In practice, however, movements between spatial locations are usually confined by the underlying road network, and hence, the actual distance between two locations can differ significantly from their $$L_p$$ L p distance. Motivated by the deficiency of the existing techniques, this paper presents a comprehensive study on OL queries in road networks. We propose a unified framework that addresses three variants of OL queries that find important applications in practice, and we instantiate the framework with several novel query processing algorithms. We further extend our framework to efficiently monitor the OLs when locations for facilities and/or clients have been updated. Our dynamic update methods lead to efficient answering of continuous optimal location queries. We demonstrate the efficiency of our solutions through extensive experiments with large real data.

ALPS: an efficient algorithm for top-k spatial preference search in road networks

In this paper, we study the processing of top-k spatial preference queries in road networks. A top-k spatial preference query retrieves a ranked list of the k best data objects based on the scores (e.g., qualities) of feature objects in their spatial neighborhoods. Several solutions have been proposed for top-k spatial preference queries in Euclidean space. However, far too little attention has been paid to top-k spatial preference queries in road networks, where the distance between two points is defined by the length of the shortest path connecting them. A simple way to answer top-k spatial preference queries is to examine the scores of feature objects in the proximity of each data object before returning a ranked list of the k best data objects. However, this simple method causes intolerable computation delays, thus rendering online processing inapplicable. Therefore, in this paper, we address this problem by presenting a new algorithm, called ALPS, for top-k spatial preference searches in road networks. Our experimental results demonstrate the superiority and effectiveness of ALPS for a wide range of problem settings.

Efficiently Monitoring Reverse k-Nearest Neighbors in Spatial Networks

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

A new spatio-temporal prediction approach based on aggregate queries

The prediction of spatio-temporal data streams which is based on aggregate queries has been an important research direction in the research field of databases. More and more methods have been proposed to obtain approximate aggregate results. However, they will consume a lot of time and storage space. This paper proposes Dynamic Sketch DS index by using modified method of Adaptive Multi-dimensional Histogram AMH* to intelligently partition static sketch which can improve the approximate quality of aggregate queries in road networks. Then, based on DS index, this paper proposes a new prediction approach over data streams in road networks using Self-Adaptive Exponential Smoothing SAES.

A safe exit algorithm for continuous nearest neighbor monitoring in road networks

Query processing in road networks has been studied extensively in recent years. However, the processing of moving queries in road networks has received little attention. In this paper, we introduce a new algorithm called the Safe Exit Algorithm SEA, which can efficiently compute the safe exit points of a moving nearest neighbor NN query on road networks. The safe region of a query is an area where the query result remains unchanged, provided that the query remains inside the safe region At each safe exit point, the safe region of a query and its non-safe region meet so that a set of safe exit points represents the border of the safe region. Before reaching a safe exit point, the client query object does not have to request the server to re-evaluate the query This significantly reduces the server processing costs and the communication costs between the server and moving clients. Extensive experimental results show that SEA outperforms a conventional algorithm by up to two orders of magnitude in terms of communication costs and computation costs

A Safe Exit Algorithm for Continuous Nearest Neighbor Monitoring in Road Networks

Query processing in road networks has been studied extensively in recent years. However, the processing of moving queries in road networks has received little attention. In this paper, we introduce a new algorithm called the Safe Exit Algorithm (SEA), which can efficiently compute the safe exit points of a moving nearest neighbor (NN) query on road networks. The safe region of a query is an area where the query result remains unchanged, provided that the query remains inside the safe region At each safe exit point, the safe region of a query and its non-safe region meet so that a set of safe exit points represents the border of the safe region. Before reaching a safe exit point, the client (query object) does not have to request the server to re-evaluate the query This significantly reduces the server processing costs and the communication costs between the server and moving clients. Extensive experimental results show that SEA outperforms a conventional algorithm by up to two orders of magnitude in terms of communication costs and computation costs.

A safe exit algorithm for continuous nearest neighbor monitoring in road networks

Query processing in road networks has been studied extensively in recent years. However, the processing of moving queries in road networks has received little attention. In this paper, we introduce...

Spatial query processing in road networks for wireless data broadcast

Recently, wireless broadcast environments have attracted significant attention due to its high scalability to broadcast information to a large number of mobile subscribers. It is especially a promising and desirable dissemination method for the heavily loaded environment where a great number of the same type of requests are sent from the users. There have been many studies on processing spatial queries via broadcast model recently. However, not much attention is paid to the spatial queries in road networks on wireless broadcast environments. In this paper, we focus on three common types of spatial queries, namely, k nearest neighbor (kNN) queries, range queries and reverse nearest neighbor (RNN) queries in spatial networks for wireless data broadcast. Specially, we propose a novel index for spatial queries in wireless broadcast environments (ISW). With the reasonable organization and the effectively pre-computed bounds, ISW provides a powerful framework for spatial queries. Furthermore, efficient algorithms are designed to cope with kNN, range and RNN queries separately based on ISW. The search space can be obviously reduced and subsequently the client can download as less as possible data for query processing, which can conserve the energy while not significantly influence the efficiency. The detailed theory analysis of cost model and the experimental results are presented for verifying the efficiency and effectiveness of ISW and our methods.

Continuous distance-based skyline queries in road networks

In recent years, the research community has introduced various methods for processing skyline queries in road networks. A skyline query retrieves the skyline points that are not dominated by others in terms of static and dynamic attributes (i.e., the road distance). This paper addresses the issue of efficiently processing continuous skyline queries in road networks. Two novel and important distance-based skyline queries are presented, namely, the continuousdε-skylinequery (Cdε-SQ) and the continuous k nearest neighbor-skyline query (Cknn-SQ). A grid index is first designed to effectively manage the information of data objects and then two algorithms are proposed, the Cdε-SQalgorithm and the Cdε-SQ+algorithm, which are combined with the grid index to answer the Cdε-SQ. Similarly, the Cknn-SQ algorithm and the Cknn-SQ+algorithm are developed to efficiently process the Cknn-SQ. Extensive experiments using real road network datasets demonstrate the effectiveness and the efficiency of the proposed algorithms.

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

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