The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Hybrid index structures for location-based web search
Proceedings of the 14th ACM international conference on Information and knowledge management
Efficient query processing in geographic web search engines
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Processing Spatial-Keyword (SK) Queries in Geographic Information Retrieval (GIR) Systems
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Efficient Merging and Filtering Algorithms for Approximate String Searches
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Fast Indexes and Algorithms for Set Similarity Selection Queries
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Keyword Search on Spatial Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient retrieval of the top-k most relevant spatial web objects
Proceedings of the VLDB Endowment
Fuzzy keyword search on spatial data
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Spatio-textual indexing for geographical search on the web
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Multi-approximate-keyword routing in GIS data
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Many Web sites support keyword search on their spatial data, such as business listings and photos. In these systems, inconsistencies and errors can exist in both queries and the data. To bridge the gap between queries and data, it is important to support approximate keyword search on spatial data. In this paper we study how to answer such queries efficiently. We focus on a natural index structure that augments a tree-based spatial index with capabilities for approximate keyword search. We systematically study how to efficiently combine these two types of indexes, and how to search the resulting index to find answers. We develop three algorithms for constructing the index, successively improving the time and space efficiency by exploiting the textual and spatial properties of the data. We experimentally demonstrate the efficiency of our techniques on real, large datasets.