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Improving geospatial query performance of an interoperable geographic situation‐awareness system for disaster response / Chuanrong Zhang in Transactions in GIS, Vol 24 n° 2 (April 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 2 (April 2020) . - pp 508 - 525 Titre : Improving geospatial query performance of an interoperable geographic situation‐awareness system for disaster response Type de document : Article/Communication Auteurs : Chuanrong Zhang, Auteur ; Tian Zhao, Auteur ; E. Lynn Usery, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 508 - 525 Note générale : Bibliographie Langues : Anglais (eng) Résumé : (Auteur) Disaster response operations require fast and coordinated actions based on real‐time disaster situation information. Although crowdsourced geospatial data applications have been demonstrated to be valuable tools for gathering real‐time disaster situation information, they only provide limited utility for disaster response coordination because of the lack of semantic compatibility and interoperability. To help overcome the semantic incompatibility and heterogeneity problems, we use Geospatial Semantic Web (GSW) technologies. We then combine GSW technologies with Web Feature Service requests to access multiple servers. However, a GSW‐based geographic information system often has poor performance due to the complex geometric computations required. The objective of this research is to explore how to use optimization techniques to improve the performance of an interoperable geographic situation‐awareness system (IGSAS) based on GSW technologies for disaster response. We conducted experiments to evaluate various client‐side optimization techniques for improving the performance of an IGSAS prototype for flood disaster response in New Haven, Connecticut. Our experimental results show that the developed prototype can greatly reduce the runtime costs of geospatial semantic queries through on‐the‐fly spatial indexing, tile‐based rendering, efficient algorithms for spatial join, and caching, especially for those spatial‐join geospatial queries that involve a large number of spatial features and heavy geometric computation. Numéro de notice : A2020-175 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12614 Date de publication en ligne : 17/02/2020 En ligne : https://doi.org/10.1111/tgis.12614 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94903 [article]