Keyword Search on Spatial Databases

• Published in: Data engineering pp. 656 - 665
• Main Authors: De Felipe, I., Hristidis, V., Rishe, N.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2008
• Subjects: Data structures; Displays; Indexing; Information retrieval; Internet; Keyword search; Nearest neighbor searches; Neural networks; Scalability; Spatial databases
• ISBN: 9781424418367; 1424418364
• ISSN: 1063-6382
• DOI: 10.1109/ICDE.2008.4497474

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 IR 2 -Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR 2 -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.