Spatial query processing in an object-oriented database system
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Gray Codes for Partial Match and Range Queries
IEEE Transactions on Software Engineering
Redundancy in spatial databases
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Fractals for secondary key retrieval
PODS '89 Proceedings of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
The design and analysis of spatial data structures
The design and analysis of spatial data structures
Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Towards an analysis of range query performance in spatial data structures
PODS '93 Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Beyond uniformity and independence: analysis of R-trees using the concept of fractal dimension
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Multidimensional access methods
ACM Computing Surveys (CSUR)
Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Designing and mining multi-terabyte astronomy archives: the Sloan Digital Sky Survey
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
An effective way to represent quadtrees
Communications of the ACM
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
PROBE Spatial Data Modeling and Query Processing in an Image Database Application
IEEE Transactions on Software Engineering
How to Avoid Building DataBlades(r) That Know the Value of Everything and the Cost of Nothing
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Accurate Estimation of the Cost of Spatial Selections
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Adaptive index management for future location-based queries
Journal of Systems and Software
Efficiently managing large-scale raster species distribution data in PostgreSQL
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Multiresolution query optimization in an online environment
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
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The linear quadtree is a spatial access method that is built by decomposing the spatial objects in a database into quadtree blocks and storing these quadtree blocks in a B-tree. The linear quadtree is very useful for geographic information systems because it provides good query performance while using existing B-tree implementations. An algorithm and a cost model are presented for processing window queries in linear quadtrees. The algorithm can handle query windows of any shape in the general case of spatial databases with overlapping objects. The algorithm recursively decomposes the space into quadtree blocks, and uses the quadtree blocks overlapping the query window to search the B-tree. The cost model estimates the I/O cost of processing window queries using the algorithm. The cost model is also based on a recursive decomposition of the space, and it uses very simple parameters that can easily be maintained in the database catalog. Experiments with real and synthetic data sets verify the accuracy of the cost model.