, Volume 13, Issue 4, pp 483–514 | Cite as

Web data retrieval: solving spatial range queries using k-nearest neighbor searches

  • Wan D. BaeEmail author
  • Shayma Alkobaisi
  • Seon Ho Kim
  • Sada Narayanappa
  • Cyrus Shahabi


As Geographic Information Systems (GIS) technologies have evolved, more and more GIS applications and geospatial data are available on the web. Spatial objects in a given query range can be retrieved using spatial range query − one of the most widely used query types in GIS and spatial databases. However, it can be challenging to retrieve these data from various web applications where access to the data is only possible through restrictive web interfaces that support certain types of queries. A typical scenario is the existence of numerous business web sites that provide their branch locations through a limited “nearest location” web interface. For example, a chain restaurant’s web site such as McDonalds can be queried to find some of the closest locations of its branches to the user’s home address. However, even though the site has the location data of all restaurants in, for example, the state of California, it is difficult to retrieve the entire data set efficiently due to its restrictive web interface. Considering that k-Nearest Neighbor (k-NN) search is one of the most popular web interfaces in accessing spatial data on the web, this paper investigates the problem of retrieving geospatial data from the web for a given spatial range query using only k-NN searches. Based on the classification of k-NN interfaces on the web, we propose a set of range query algorithms to completely cover the rectangular shape of the query range (completeness) while minimizing the number of k-NN searches as possible (efficiency). We evaluated the efficiency of the proposed algorithms through statistical analysis and empirical experiments using both synthetic and real data sets.


Range queries k-Nearest neighbor queries Web data Web interfaces Web integration GIS 



The authors thank Prof. Petr Vojtěchovský for providing helpful suggestions and Brandon Haenlein for the valuable discussions throughout this work.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Wan D. Bae
    • 1
    Email author
  • Shayma Alkobaisi
    • 2
  • Seon Ho Kim
    • 3
  • Sada Narayanappa
    • 4
  • Cyrus Shahabi
    • 5
  1. 1.Department of Mathematics, Statistics and Computer ScienceUniversity of Wisconsin-StoutMenomonieUSA
  2. 2.College of Information TechnologyUnited Arab Emirates UniversityAl-AinUnited Arab Emirates
  3. 3.Department of Computer Science & Information TechnologyUniversity of District of ColumbiaWashingtonUSA
  4. 4.Department of Computer ScienceUniversity of DenverDenverUSA
  5. 5.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA

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