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

In smart phones, vehicles and wearable devices, GPS sensors are ubiquitous and collect a lot of valuable spatial data from the real world. Given a set of weighted points and a rectangle r in the space, a maximizing range sum (MaxRS) query is to find the position of r , so as to maximize the total we...

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Bibliographic Details
Published in:Journal of computer science and technology 2022-10, Vol.37 (5), p.1005-1025
Main Authors: Chen, Jian, Zhang, Kai-Qi, Ren, Tian, Wu, Zhen-Qing, Gao, Hong
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Data Structures and Information Theory
Euclidean geometry
Euclidean space
Geospatial data
Industrial locations
Information Systems Applications (incl.Internet)
Networks
Queries
Rectangles
Regular Paper
Roads
Smartphones
Software Engineering
Spatial data
Sums
Theory of Computation
Wearable technology
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Summary:In smart phones, vehicles and wearable devices, GPS sensors are ubiquitous and collect a lot of valuable spatial data from the real world. Given a set of weighted points and a rectangle r in the space, a maximizing range sum (MaxRS) query is to find the position of r , so as to maximize the total weight of the points covered by r (i.e., the range sum). It has a wide spectrum of applications in spatial crowdsourcing, facility location and traffic monitoring. Most of the existing research focuses on the Euclidean space; however, in real life, the user’s moving route is constrained by the road network, and the existing MaxRS query algorithms in the road network are inefficient. In this paper, we propose a novel GPU-accelerated algorithm, namely, GAM, to tackle MaxRS queries in road networks in two phases efficiently. In phase 1, we partition the entire road network into many small cells by a grid and theoretically prove the correctness of parallel query results by grid shifting, and then we propose an effective multi-grained pruning technique, by which the majority of cells can be pruned without further checking. In phase 2, we design a GPU-friendly storage structure, cell-based road network (CRN), and a two-level parallel framework to compute the final result in the remaining cells. Finally, we conduct extensive experiments on two real-world road networks, and the experimental results demonstrate that GAM is on average one order faster than state-of-the-art competitors, and the maximum speedup can achieve about 55 times.
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-022-2330-3