Next Article in Journal
Pioneering GML Deployment for NSDI — Case Study of USTIGER/GML
Previous Article in Journal
Assessing the Geographic Representativity of Farm Accountancy Data
Article Menu

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2013, 2(1), 67-81; doi:10.3390/ijgi2010067

Towards Improving Query Performance of Web Feature Services (WFS) for Disaster Response

1
Department of Geography & Center of Environmental Sciences and Engineering, University of Connecticut, Storrs, CT 06269-4148, USA
2
Department of Computer Science, University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
*
Author to whom correspondence should be addressed.
Received: 6 January 2013 / Revised: 21 January 2013 / Accepted: 31 January 2013 / Published: 6 February 2013
View Full-Text   |   Download PDF [757 KB, uploaded 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 disaster response; WFS; performance; Voronoi diagram index; parallel computation
Figures

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top