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
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Constrained Nearest Neighbor Queries
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Hybrid index structures for location-based web search
Proceedings of the 14th ACM international conference on Information and knowledge management
Inverted files for text search engines
ACM Computing Surveys (CSUR)
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
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Voronoi-based K nearest neighbor search for spatial network databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Continuous visible nearest neighbor queries
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Keyword Search on Spatial Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Keyword Search in Spatial Databases: Towards Searching by Document
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Monitoring Orientation of Moving Objects around Focal Points
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Efficient retrieval of the top-k most relevant spatial web objects
Proceedings of the VLDB Endowment
Efficient processing of top-k spatial preference queries
Proceedings of the VLDB Endowment
Semantic linking through spaces for cyber-physical-socio intelligence: A methodology
Artificial Intelligence
Reverse spatial and textual k nearest neighbor search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Location-aware type ahead search on spatial databases: semantics and efficiency
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Collective spatial keyword querying
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Efficient continuously moving top-k spatial keyword query processing
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Probabilistic Resource Space Model for Managing Resources in Cyber-Physical Society
IEEE Transactions on Services Computing
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With the rocket development of the Internet, WWW(World Wide Web), mobile computing and GPS (Global Positioning System) services, location-based services like Web GIS (Geographical Information System) portals are becoming more and more popular. Spatial keyword queries over GIS spatial data receive much more attention from both academic and industry communities than ever before. In general, a spatial keyword query containing spatial location information and keywords is to locate a set of spatial objects that satisfy the location condition and keyword query semantics. Researchers have proposed many solutions to various spatial keyword queries such as top-K keyword query, reversed kNN keyword query, moving object keyword query, collective keyword query, etc. In this paper, we propose a density-based spatial keyword query which is to locate a set of spatial objects that not only satisfies the query's textual and distance condition, but also has a high density in their area. We use the collective keyword query semantics to find in a dense area, a group of spatial objects whose keywords collectively match the query keywords. To efficiently process the density based spatial keyword query, we use an IR-tree index as the base data structure to index spatial objects and their text contents and define a cost function over the IR-tree indexing nodes to approximately compute the density information of areas. We design a heuristic algorithm that can efficiently prune the region according to both the distance and region density in processing a query over the IR-tree index. Experimental results on datasets show that our method achieves desired results with high performance.