Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Partition based spatial-merge join
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
IEEE Transactions on Knowledge and Data Engineering
A Robust and Self-tuning Page-Replacement Strategy for Spatial Database Systems
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Scalable Sweeping-Based Spatial Join
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A Cost Model for Estimating the Performance of Spatial Joins Using R-trees
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
The Sort/Sweep Algorithm: A New Method for R-tree Based Spatial Joins
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Data Redundancy and Duplicate Detection in Spatial Join Processing
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
ACM Transactions on Database Systems (TODS)
Complex Spatial Query Processing
Geoinformatica
Multi-Way Distance Join Queries in Spatial Databases
Geoinformatica
A spatial hash join algorithm suited for small buffer size
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Self-tuning cost modeling of user-defined functions in an object-relational DBMS
ACM Transactions on Database Systems (TODS)
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This paper presents a query optimizer module based on cost estimation that chooses the best filtering step algorithm to perform a specific spatial join operation. A set of expressions to predict the number of I/O operations and the response time of each algorithm is first presented and later refined considering a given hardware configuration. The query optimizer chooses the algorithm that returns the smaller estimated response time. In order to evaluate the query optimizer, we carried out a set of tests with synthetic and real data sets, in a significant number of different scenarios. The query optimizer correctly chooses the fastest algorithm in almost 90% of submitted operations, with minimal overhead.