Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80050
Title: Efficient visibility analysis for massive observers
Authors: Wang, W
Tang, B 
Fan, X
Mao, H
Yang, H
Zhu, M
Keywords: MapReduce
R
Regular square grids
Scalability
Visibility analysis
Issue Date: 2017
Publisher: Elsevier
Source: Procedia computer science, 2017, v. 111, p. 120-128 How to cite?
Journal: Procedia computer science 
Abstract: Many applications in Geographic Information System (GIS) apply visibility analysis as a key subroutine, and thus the time spent on visibility analysis is the bottleneck for all these applications, such as navigation, aviation, landscape, and military etc. The new challenge to the scalability of visibility analysis for large datasets shows, most of academic works in GIS only consider a few thousands of observer objects, while many works in industry and science have to face on millions (even billions) of observer objects. In this paper, we devise a novel computation framework which consists of three components, i.e., optimized line-of-sight algorithm, R∗-tree filter and MapReduce-based segmented computation. The proposed solution can support GIS systems to conduct efficient visibility analysis for massive observers. Finally, we demonstrate the efficiency and the scalability of our proposed solutions by synthetic datasets. The results show that our proposed solution achieves at least an order of magnitude speedup over existing solutions.
Description: 8th International Conference on Advances in Information Technology, IAIT 2016, 19-22 December 2016
URI: http://hdl.handle.net/10397/80050
ISSN: 1877-0509
DOI: 10.1016/j.procs.2017.06.018
Rights: © 2017 The Authors. Published by Elsevier B.V
Peer-review under responsibility of the organizing committee of the 8th International Conference on Advances in Information Technology
The following publication Wang, W., Tang, B., Fan, X., Mao, H., Yang, H., & Zhu, M. (2017). Efficient visibility analysis for massive observers. Procedia computer science, 2017, 111, 120-128 is available at https://dx.doi.org/10.1016/j.procs.2017.06.018
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