Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/80050
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Wang, W | - |
dc.creator | Tang, B | - |
dc.creator | Fan, X | - |
dc.creator | Mao, H | - |
dc.creator | Yang, H | - |
dc.creator | Zhu, M | - |
dc.date.accessioned | 2018-12-21T07:14:46Z | - |
dc.date.available | 2018-12-21T07:14:46Z | - |
dc.identifier.issn | 1877-0509 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/80050 | - |
dc.description | 8th International Conference on Advances in Information Technology, IAIT 2016, 19-22 December 2016 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2017 The Authors. Published by Elsevier B.V | en_US |
dc.rights | Peer-review under responsibility of the organizing committee of the 8th International Conference on Advances in Information Technology | en_US |
dc.rights | Every peer-reviewed research article appearing in Procedia Computer Science will be published open access and under Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND)(https://creativecommons.org/licenses/by-nc-nd/4.0/) | en_US |
dc.rights | 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 | en_US |
dc.subject | MapReduce | en_US |
dc.subject | R | en_US |
dc.subject | Regular square grids | en_US |
dc.subject | Scalability | en_US |
dc.subject | Visibility analysis | en_US |
dc.title | Efficient visibility analysis for massive observers | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 120 | en_US |
dc.identifier.epage | 128 | en_US |
dc.identifier.volume | 111 | en_US |
dc.identifier.doi | 10.1016/j.procs.2017.06.018 | en_US |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Procedia computer science, 2017, v. 111, p. 120-128 | - |
dcterms.isPartOf | Procedia computer science | - |
dcterms.issued | 2017 | - |
dc.identifier.scopus | 2-s2.0-85029360473 | - |
dc.relation.conference | International Conference on Advances in Information Technology [IAIT] | - |
dc.description.validate | 201812 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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Wang_Analysis_Massive_Observers.pdf | 1.28 MB | Adobe PDF | View/Open |
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