Abstract
The paper focuses on the design of an optimum method for handling the continuous skyline query problem in road networks. Existing studies on processing the continuous skyline query focus exclusively on static road networks, which are limited because the state of roads in road networks is constantly changing. Therefore, to apply current methods for dynamically weighted road networks, a distributed skyline query method based on a grid partition method has been proposed in this paper. The method adopts the concepts of a distributed computing framework and road network preprocessing computations in which multiple parallel computing nodes are allocated and organized in grids. Using this approach, the road network map is simplified to a hub graph with much smaller scale such that the query load of the central node can be significantly reduced. The theoretical analysis and experimental results both indicate that, by using the proposed method, the system can achieve quick response time for users as well as a good balance between response times and accuracy. Therefore, it can be concluded that using the proposed method is beneficial for handling continuous skyline queries in a dynamically weighted road network.
Highlights
Providing location-based services (LBSs) [1] for moving objects in road networks has become an important research topic in traffic informatization and intelligent traffic system development [2] due to the advances in wireless communication and global positioning system (GPS) technologies
grid-skyline query (Gsky) Skyline query with precomputing SRsky (Psky) Dsky computational load between the nodes, the road network may be divided into grids unevenly such that each grid contains a similar number of vertices
The system used Hadoop Online Prototype (HOP) [29] for Gsky, where one PC served as the central node and the remaining five PCs served as computing nodes
Summary
Providing location-based services (LBSs) [1] for moving objects in road networks has become an important research topic in traffic informatization and intelligent traffic system development [2] due to the advances in wireless communication and global positioning system (GPS) technologies. The first approach is on-the-fly, that is, to identify the two objects as two vertices in the road network and to use a shortest path algorithm (e.g., Dijkstra’s algorithm) to perform direct calculations This method can be applied to a dynamically weighted road network, but the required computation rapidly increases as the scale of the road network increases. The shortest distance between two objects can be obtained by querying the shortest distance between the nearest neighbor vertices of each object This approach results in faster query response times, but it cannot be applied to a dynamically weighted road network because of the huge cost from precomputation. The central node only needs to maintain the topology structure of the grids, thereby reducing data maintenance for the central node
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