Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Space/time trade-offs in hash coding with allowable errors
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
Exploiting Spatial Indexes for Semijoin-Based Join Processing in Distributed Spatial Databases
IEEE Transactions on Knowledge and Data Engineering
A Parallel Spatial Join Processing for Distributed Spatial Databases
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
Spatial Join Strategies in Distributed Spatial DBMS
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Optimizing distributed spatial joins
Optimizing distributed spatial joins
Optimizing distributed spatial joins using R-Trees
Proceedings of the 43rd annual Southeast regional conference - Volume 1
ACM Transactions on Database Systems (TODS)
Optimization Algorithms for Distributed Queries
IEEE Transactions on Software Engineering
BR-Tree: A Scalable Prototype for Supporting Multiple Queries of Multidimensional Data
IEEE Transactions on Computers
Principles of Distributed Database Systems
Principles of Distributed Database Systems
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In this paper, we present a novel strategy for distributed spatial query optimization that involves multiple sites. Most previous work in the area of distributed spatial query processing and optimization focuses only on strategies for performing spatial joins and spatial semijoins, and distributed spatial queries that only involve two sites. We propose a new strategy, called the Restricted strategy, for optimizing a distributed spatial query. It uses spatial semijoins and can involve any number of sites in a distributed spatial database. The Restricted strategy improves upon an existing strategy by sending group approximations, instead of sending approximations for all objects, in order to reduce the number of comparisons between objects and thereby minimize the CPU and data transmission cost. A performance evaluation of our strategy finds that it significantly minimizes the number of data comparisons and CPU time of distributed spatial queries.