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Towards Improving Query Performance of Web Feature Services (WFS) for Disaster Response
Department of Geography & Center of Environmental Sciences and Engineering, University of Connecticut, Storrs, CT 06269-4148, USA
Department of Computer Science, University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
* Author to whom correspondence should be addressed.
Received: 6 January 2013; in revised form: 21 January 2013 / Accepted: 31 January 2013 / Published: 6 February 2013
Abstract: While OGC’s WFS facilitates disseminating heterogeneous spatial data over the Web and allows feature-level geospatial information sharing and synchronization, performance issues challenge the efficient and effective utilization of WFS for disaster response. Literature shows that obtaining spatial information becomes very slow when querying WFS systems from large geospatial databases over the Internet. Solutions on how to improve the WFS system performance so that spatial data can be delivered to disaster responders within a reasonable amount of time are needed. This paper proposes a parallel approach based on Voronoi diagram indexing and data/task parallelism for improving the query performance of WFS systems for disaster applications. Experimental results show that the parallel approach can significantly improve the response time needed to process the spatial queries from a massive volume of spatial data for disaster response.
Keywords: disaster response; WFS; performance; Voronoi diagram index; parallel computation
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MDPI and ACS Style
Zhang, C.; Zhao, T.; Li, W. Towards Improving Query Performance of Web Feature Services (WFS) for Disaster Response. ISPRS Int. J. Geo-Inf. 2013, 2, 67-81.
Zhang C, Zhao T, Li W. Towards Improving Query Performance of Web Feature Services (WFS) for Disaster Response. ISPRS International Journal of Geo-Information. 2013; 2(1):67-81.
Zhang, Chuanrong; Zhao, Tian; Li, Weidong. 2013. "Towards Improving Query Performance of Web Feature Services (WFS) for Disaster Response." ISPRS Int. J. Geo-Inf. 2, no. 1: 67-81.