Keyword Search on Spatial Databases

  • Authors:

  • Ian De Felipe;Vagelis Hristidis;Naphtali Rishe

  • Affiliations:

  • School of Computing and Information Sciences, Florida International University, Miami FL 33199. ian.de.felipe@cis.fiu.edu;School of Computing and Information Sciences, Florida International University, Miami FL 33199. vagelis@cis.fiu.edu;School of Computing and Information Sciences, Florida International University, Miami FL 33199. rishen@cis.fiu.edu

  • Venue:
  • ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
  • Year:
  • 2008

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Abstract

Many applications require finding objects closest to a specified location that contains a set of keywords. For example online yellow pages allow users to specify an address and a set of keywords. In return the user obtains a list of businesses whose description contains these keywords ordered by their distance from the specified address. The problems of nearest neighbor search on spatial data and keyword search on text data have been extensively studied separately. However to the best of our knowledge there is no efficient method to answer spatial keyword queries that is queries that specify both a location and a set of keywords. In this work we present an efficient method to answer top-k spatial keyword queries. To do so we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree and use it to answer top-k spatial keyword queries. Our algorithms are experimentally compared to current methods and are shown to have superior performance and excellent scalability.