Scalable Spatial Join for WFS Clients (Short Paper)

Authors Tian Zhao, Chuanrong Zhang, Zhijie Zhang



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Author Details

Tian Zhao
  • University of Wisconsin - Milwaukee, Milwaukee, WI, USA
Chuanrong Zhang
  • University of Connecticut, Storrs, CT, USA
Zhijie Zhang
  • University of Connecticut, Storrs, CT, USA

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Tian Zhao, Chuanrong Zhang, and Zhijie Zhang. Scalable Spatial Join for WFS Clients (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 72:1-72:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.72

Abstract

Web Feature Service (WFS) is a popular Web service for geospatial data, which is represented as sets of features that can be queried using the GetFeature request protocol. However, queries involving spatial joins are not efficiently supported by WFS server implementations such as GeoServer. Performing spatial join at client side is unfortunately expensive and not scalable. In this paper, we propose a simple and yet scalable strategy for performing spatial joins at client side after querying WFS data. Our approach is based on the fact that Web clients of WFS data are often used for query-based visual exploration. In visual exploration, the queried spatial objects can be filtered for a particular zoom level and spatial extent and be simplified before spatial join and still serve their purpose. This way, we can drastically reduce the number of spatial objects retrieved from WFS servers and reduce the computation cost of spatial join, so that even a simple plane-sweep algorithm can yield acceptable performance for interactive applications.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
Keywords
  • WFS
  • SPARQL
  • Spatial Join

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References

  1. L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Scalable sweeping based spatial join. In Proceedings of the 24th International Conference on Very Large Data Bases (VLDB), pages 570-581, New York, August 1998. Google Scholar
  2. T. Brinkho, H.-P. Kriegel, and B. Seeger. Efficient processing of spatial joins using rtrees. In Proceedings of the ACM SIGMOD Conference, pages 237-246, May 1993. Google Scholar
  3. R. Elmasri and S. B. Navathe. Fundamentals of Database Systems. Addison-Wesley, Reading, MA, third edition edition, 2000. Google Scholar
  4. P. Mishra and M. H. Eich. Join processing in relational databases. ACM Computing Surveys, 24(1):63-113, March 1992. Google Scholar
  5. Cyrus Shahabi, Mohammad R. Kolahdouzan, and Maytham Safar. Alternative strategies for performing spatial joins on web sources. Knowl. Inf. Syst., 6(3):290-314, May 2004. Google Scholar
  6. J. Zhang, S. You, and L. Gruenwald. Towards GPU-accelerated Web-GIS for query-driven visual exploration. In Proceedings of the 15th International Symposium on Web and Wireless Geographical Information Systems, pages 119-136, Shanghai, China, May 2017. Google Scholar
  7. T. Zhao, C. Zhang, and W. Li. Accessing distributed WFS data through a RDF query interface. In Proceedings of GIScience, 2016. Google Scholar
  8. T. Zhao, C. Zhang, and W. Li. Adaptive and optimized RDF query interface for distributed WFS data. International Journal of Geo-Information, 6(4), 2017. Google Scholar