Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80050
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dc.contributorDepartment of Computing-
dc.creatorWang, W-
dc.creatorTang, B-
dc.creatorFan, X-
dc.creatorMao, H-
dc.creatorYang, H-
dc.creatorZhu, M-
dc.date.accessioned2018-12-21T07:14:46Z-
dc.date.available2018-12-21T07:14:46Z-
dc.identifier.issn1877-0509en_US
dc.identifier.urihttp://hdl.handle.net/10397/80050-
dc.description8th International Conference on Advances in Information Technology, IAIT 2016, 19-22 December 2016en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2017 The Authors. Published by Elsevier B.Ven_US
dc.rightsPeer-review under responsibility of the organizing committee of the 8th International Conference on Advances in Information Technologyen_US
dc.rightsEvery 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.rightsThe 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.2017.06.018en_US
dc.subjectMapReduceen_US
dc.subjectRen_US
dc.subjectRegular square gridsen_US
dc.subjectScalabilityen_US
dc.subjectVisibility analysisen_US
dc.titleEfficient visibility analysis for massive observersen_US
dc.typeConference Paperen_US
dc.identifier.spage120en_US
dc.identifier.epage128en_US
dc.identifier.volume111en_US
dc.identifier.doi10.1016/j.procs.2017.06.018en_US
dcterms.abstractMany 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.accessRightsopen accessen_US
dcterms.bibliographicCitationProcedia computer science, 2017, v. 111, p. 120-128-
dcterms.isPartOfProcedia computer science-
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85029360473-
dc.relation.conferenceInternational Conference on Advances in Information Technology [IAIT]-
dc.description.validate201812 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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