Queries In Road Networks Research Articles

Distributed MapReduce processing of location-based aggregate queries in road networks

Distributed MapReduce processing of location-based aggregate queries in road networks

PCSP: Efficiently Answering Label-Constrained Shortest Path Queries in Road Networks

Shortest path queries are ubiquitous in many spatial applications. Existing solutions assign numerical weights to edges and compute the path with the minimum sum of edge weights. However, in practice, the road categories associated with edges (e.g., toll) can make shortest paths undesirable, e.g., they may use unfavorable toll roads. Augmenting each edge with a label to denote its category, we study the Label-Constrained Shortest Path (LCSP) query that finds the shortest path under the constraint that the edge labels along the path should follow a pattern expressed by a formal language. There have been extensive LCSP solutions, but they are either inefficient in query processing or limited to special languages with low expressiveness capacity. In this paper, we propose the index called Partially Constrained Shortest Path (PCSP), which answers each query quickly by concatenating two shortest paths that partially satisfy the constraint and support more general regular languages. We also present pruning techniques that further optimize query efficiency. Experimental comparison with the state-of-the-art index demonstrates the superiority of PCSP. It can answer each LCSP query in around 100 microseconds and runs faster than the best-known solution by up to two orders of magnitude.

Safest Nearby Neighbor Queries in Road Networks

Safety on the roads has become a major concern in recent days. Travellers prefer to avoid road inconveniences that may occur from crime incidents, street harassment, protests or riots during unrest in a country. To facilitate safe travel, we introduce a novel query for road networks called the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> safest nearby neighbor (SNN) query. Given a query location <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v_l$</tex-math> </inline-formula> , a distance constraint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_c$</tex-math> </inline-formula> and a point of interest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_i$</tex-math> </inline-formula> , we define the safest path from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v_l$</tex-math> </inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_i$</tex-math> </inline-formula> as the path with the highest path safety score among all the paths from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v_l$</tex-math> </inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_i$</tex-math> </inline-formula> with length less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_c$</tex-math> </inline-formula> . The path safety score is computed considering the road safety of each road segment on the path. Given a query location <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v_l$</tex-math> </inline-formula> , a distance constraint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_c$</tex-math> </inline-formula> and a set of POIs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$</tex-math> </inline-formula> , a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> SNN query returns <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> POIs with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> highest path safety scores in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$</tex-math> </inline-formula> along with their respective safest paths from the query location. We develop two novel indexing structures called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Ct$</tex-math> </inline-formula> -tree and a safety score based Voronoi diagram (SNVD). We propose two efficient query processing algorithms each exploiting one of the proposed indexes to effectively refine the search space using the properties of the index. Our extensive experimental study on real datasets demonstrates that our solution is on average an order of magnitude faster than the baseline.

GAM: A GPU-Accelerated Algorithm for MaxRS Queries in Road Networks

GAM: A GPU-Accelerated Algorithm for MaxRS Queries in Road Networks

The Efficient Processing of Moving k-Farthest Neighbor Queries in Road Networks

Given a set of facilities F and a query point q, a k-farthest neighbor (kFN) query returns the k farthest facilities f1,f1,⋯,fk from q. This study considers the moving k-farthest neighbor (MkFN) query that constantly retrieves the k facilities farthest from a moving query point q in a road network. The main challenge in processing MkFN queries in road networks is avoiding the repeated retrieval of candidate facilities as the query point arbitrarily moves along the road network. To this end, this study proposes a moving farthest search algorithm (MOFA) to compute valid segments for the query segment in which the query point is located. Each valid segment has the same k facilities farthest from the query locations in the valid segment. Therefore, MOFA retrieves candidate facilities only once for the query segment and computes valid segments using these candidate facilities, thereby avoiding the repeated retrieval of candidate facilities when the query point moves. An empirical study using real-world road networks demonstrates the superiority and scalability of MOFA compared to a conventional solution.

Processing Location-Based Aggregate Queries in Road Networks

In recent years, the research community has introduced various methods for processing the location-based queries on a single type of objects in road networks. However, in real-life applications user may be interested in obtaining information about different types of objects, in terms of their neighboring relationship. The sets of different types of objects closer to each other are termed the heterogeneous neighboring object sets (or HNO sets for short). To provide users with object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects, we present useful and important location-based aggregate queries on finding the HNO sets in road net-works. The location-based aggregate queries are the shortest average distance query (SAvgDQ), the shortest minimal distance query (SMinDQ), the shortest maximal distance query (SMaxDQ), and the shortest sum distance query (SSumDQ). We first utilize a grid index to manage information of data objects and road networks, and then propose the SAvgDQ, SMinDQ, SMaxDQ, and SSumDQ processing algorithms, which are combined with the grid index to efficiently process the four types of location-based aggregate queries, respectively. A comprehensive set of experiments is conducted to demonstrate the efficiency of the proposed processing algorithms using real road network datasets.

An Efficient Network Coding-based Air Index for Queries in Road Networks

An Efficient Network Coding-based Air Index for Queries in Road Networks

Group Processing of Multiple k-Farthest Neighbor Queries in Road Networks

Advances in mobile technologies and map-based applications enables users to utilize sophisticated spatial queries, including k-nearest neighbor and shortest path queries. Often, location-based servers are used to handle multiple simultaneous queries because of the popularity of map-based applications. This study focuses on the efficient processing of multiple concurrent k-farthest neighbor (kFN) queries in road networks. For a positive integer k, query point q, and set of data points P, a kFN query returns k data points farthest from the query point q. For addressing multiple concurrent spatial queries, traditional location-based servers based on one-query-at-a-time processing are unsuitable owing to high redundant computation costs. Therefore, we propose a group processing of multiple kFN (GMP) algorithm to process multiple kFN queries in road networks. The proposed GMP algorithm uses group computation to avoid the redundant computation of network distances between the query and data points. The experiments using real-world roadmaps demonstrate the proposed solution's effectiveness and efficiency.

Faster and Better Nested Dissection Orders for Customizable Contraction Hierarchies

Graph partitioning has many applications. We consider the acceleration of shortest path queries in road networks using Customizable Contraction Hierarchies (CCH). It is based on computing a nested dissection order by recursively dividing the road network into parts. Recently, with FlowCutter and Inertial Flow, two flow-based graph bipartitioning algorithms have been proposed for road networks. While FlowCutter achieves high-quality results and thus fast query times, it is rather slow. Inertial Flow is particularly fast due to the use of geographical information while still achieving decent query times. We combine the techniques of both algorithms to achieve more than six times faster preprocessing times than FlowCutter and even faster queries on the Europe road network. We show that, using 16 cores of a shared-memory machine, this preprocessing needs four minutes.

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.

Efficient Scheduling of Generalized Group Trips in Road Networks

In this article, we introduce generalized group trip scheduling (GGTS) queries that enable friends and families to perform activities at different points of interest (POIs), such as a shopping center, a restaurant and a pharmacy with the minimum total travel distance. Trip planning and scheduling for groups, an important class of location-based services (LBSs), have recently received attention from researchers. However, both group trip planning (GTP) and group trip scheduling (GTS) queries have restrictions: a GTP query assumes that all group members visit all required POIs together, whereas a GTS query requires that each POI is visited by a single group member. A GGTS query is more general and allows any number of group members to visit a POI together. We propose an efficient algorithm to evaluate the exact answers for GGTS queries in road networks. Since finding the answer for a GGTS query is an NP-hard problem, to reduce the processing overhead for a large group size or a large number of required POI types or a large POI dataset, we propose two heuristic solutions—trip-scheduling heuristic (TSH) and search region refinement heuristic (SRH)—for processing GGTS queries. Extensive experiments with real datasets show that our optimal algorithm is preferable for small parameter settings, and the heuristic solutions reduce the processing overhead significantly in return for sacrificing the accuracy slightly.

On Efficiently Answering Why-Not Range-Based Skyline Queries in Road Networks

The range-based skyline (r-skyline) query on road networks retrieves the skyline objects for each of the query points that are within a road region, considering the objects’ spatial and non-spatial attributes. However, reasoning about missing query results, specified by why-not questions , has not till recently received the attention it is worth of. In this paper, we systematically carry out the study of why-not questions on the r-skyline query in the road network environment (abbrev. as the why-not RSQ problem ). We present three modification strategies, including modifying the query range, modifying the why-not point, and modifying both of them, for supporting the why-not RSQ problem. We also propose three efficient algorithms to tackle the why-not RSQ problem, where several newly presented effective concepts/techniques are leveraged, such as the concepts of skyline scope and skyline dominance region , non-spatial attribute modification pruning , and G-tree index . Extensive experimental evaluation using both real and synthetic data sets demonstrates the performance of our proposed algorithms.

The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks

This paper addresses the problem of processing range monitoring queries, each of which continuously retrieves moving objects that are currently located within a given query range. In particular, this paper focuses on processing range monitoring queries in the road network, where movements of the objects are constrained by a predefined set of paths. One of the most important challenges of processing range monitoring queries is how to minimize the wireless communication cost and the server computation cost, both of which are heavily dependent on the amount of location-update stream generated by moving objects. The traditional centralized methods for range monitoring queries assume that moving objects periodically send location-updates to the server. However, when the number of moving objects becomes increasingly large, such an assumption may no longer be acceptable because the amount of location-update stream becomes enormous. Recently, some distributed methods have been proposed, where moving objects utilize their available computational capabilities for sending location-updates to the server only when necessary. Unfortunately, the existing distributed methods only deal with the objects moving in Euclidean space, and thus they cannot be extended to processing range monitoring queries over the objects moving along the road network. In this paper, we propose the distributed method for processing range monitoring queries in the road network. To utilize the computational capabilities of moving objects, we introduce the concept of vicinity region. A vicinity region, assigned to each moving object o, makes o monitor whether or not it should be included in the results of nearby queries. The proposed method includes (i) a new spatial index structure, called the Segment-based Space Partitioning tree (SSP-tree) whose role is to efficiently search the appropriate vicinity regions for moving objects based on their heterogeneous computational capabilities and (ii) the details of the communication strategy between the server and moving objects, which significantly reduce the wireless communication cost as well as the server computation cost. Through simulations, we verify the effectiveness for processing range monitoring queries over a large number of moving objects (up to 100,000) in the road network (modeled as an undirected graph).

Hybrid spatial air index for processing queries in road networks

In case of road networks, for the clients to disseminate data, wireless broadcast is a scalable and secure strategy. As far as network partition index (NPI) is concerned, it doesn’t address certain problems; as to how to handle if there has been a large number of data objects in broadcast system, in the same way how to handle link errors and cache management. Hence, to address such problems, hybrid spatial air index (HSAI) an NPI based air index has been proposed wherein hybrid broadcast scheduling, adaptive XOR-based network coding and adaptive cooperative caching are effectively used for spatial queries in road networks. Experiments are conducted for evaluating query performance and HSAI is compared with state-of-the-art NPI. The experimental results show the outperformance of HSAI.

A Novel Air Index for Range Queries in Road Networks

Objective of the present work is to improve range query performance using Hybrid Spatial Air Index (HSAI). HSAI has been designed with combination of both cache management and network coding for processing range queries in road networks. HSAI has been utilized the advantage of both cache management and network coding and reduce client search space. The experiments have been conducted for evaluating performance, the experimental results show that HSAI outperform.

Processing of K-Nearest Neighbour Queries in Road Networks using Spatial Air Index

Spatial Air Index (SAI) has been proposed for improving query performance of k-nearest neighbour queries in road networks. SAI has been effectively utilized the usage of Adaptive Cooperative Caching (ACC) and reduced search space. Experiments have been conducted for evaluated query result, the experimental result show that SAI outperform compared to state-of-the-art Network Partition Index (NPI).

Optimal hybrid broadcast scheduling and adaptive cooperative caching for spatial queries in road networks

Wireless broadcast is an efficient strategy for disseminating data to a large population of clients in road networks. Network Partition Index (NPI) does not address how to handle the environment effectively when the data objects are increased to a large number in broadcast system and physical restrictions of clients which include low bandwidth, mobility, limited energy and capacity. To address these problems, an NPI based air index, called Spatial Air Index (SAI) has been proposed by using Optimal Hybrid Broadcast (OHB) scheduling and Adaptive Cooperative Caching (ACC) for spatial queries in road networks. Experiments are conducted for evaluating query performance and comparison of SAI with state-of-the-art NPI. The experimental results show that SAI outperforms the rest.

Popularity-aware collective keyword queries in road networks

This paper addresses a popularity-aware collective keyword (PAC-K) query in road networks. Given a road network with POIs (Points of Interest), which is modeled as a road network graph, where each node locating in a two-dimensional space represents a road intersection or a POI, and each edge with weight represents a road segment, the PACK query aims to find a group of popular POIs (i.e., a popular region) that cover the query’s keywords and satisfy the distance requirements from each node to the query node and between each pair of nodes, such that the sum of rating scores over these nodes for the query keywords is maximized. We show the problem of answering the PACK query is NP-Hard. To solve this problem, we present exact and heuristic solutions on small and large road networks, respectively. In particular, to improve query performance, we propose a rating score scaling technique to reduce the search space and a redundant computation reducing technique to reduce the excessive redundant computations in query processing. Extensive performance studies using two real datasets confirm the efficiency, accuracy, and scalability of the proposed solutions.

Continuous range-based skyline queries in road networks

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.

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.