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A Map-Reduce based parallel approach for improving query performance in a geospatial semantic web for disaster response

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Abstract

Rapid retrieval of spatial information is critical to ensure that emergency supplies and resources can reach the impacted areas in the most efficient manner. However, it remains challenging to find out the needed spatial information efficiently because of the intensive geocomputation processes involved and the heterogeneity of spatial data. It is quite cost prohibitive to query the spatial information from geographical knowledge bases containing complex topological relationships. This research introduces a Map-Reduce based parallel approach for improving the query performance of a geospatial ontology for disaster response. The approach focuses on parallelizing the spatial join computations of GeoSPARQL queries. The proposed parallel approach makes full use of data/task parallelism for spatial queries. The results of some initial experiments show that the proposed approach can reduce individual spatial query execution time by taking advantage of parallel processes. The proposed approach, therefore, may afford a large number of concurrent spatial queries in disaster response applications.

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Acknowledgments

Anselin’s research was supported in part by award OCI-1047916, SI2-SSI from the U.S. National Science Foundation.

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Authors and Affiliations

  1. Department of Geography & Center of Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, 06269-4148, USA

    Chuanrong Zhang & Weidong Li

  2. Department of Computer Science, University of Wisconsin–Milwaukee, Milwaukee, WI, 53201, USA

    Tian Zhao & Ke Chen

  3. School of Geographical Sciences & Urban Planning, Arizona State University, Tempe, AZ, 85287, USA

    Luc Anselin

Authors
  1. Chuanrong Zhang
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  2. Tian Zhao
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  3. Luc Anselin
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  4. Weidong Li
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  5. Ke Chen
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Corresponding author

Correspondence to Chuanrong Zhang.

Additional information

Communicated by: H. A. Babaie

Published in the Special Issue “Intelligent GIServices” with Guest Editor Dr. Rahul Ramachandran

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Zhang, C., Zhao, T., Anselin, L. et al. A Map-Reduce based parallel approach for improving query performance in a geospatial semantic web for disaster response. Earth Sci Inform 8, 499–509 (2015). https://doi.org/10.1007/s12145-014-0179-x

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  • DOI: https://doi.org/10.1007/s12145-014-0179-x

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