Spatial query processing in an object-oriented database system
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
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
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CIKM '93 Proceedings of the second international conference on Information and knowledge management
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
Processing and optimization of multiway spatial joins using R-trees
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Query Optimization in Database Systems
ACM Computing Surveys (CSUR)
Complexity of estimating multi-way join result sizes for area skewed spatial data
Information Processing Letters
ACM Transactions on Database Systems (TODS)
An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Realm-based spatial data types: the ROSE algebra
The VLDB Journal — The International Journal on Very Large Data Bases
Cost Models for Join Queries in Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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
Reasoning about Binary Topological Relations
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
Multi-way Spatial Joins Using R-Trees: Methodology and Performance Evaluation
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
BEAST: a buffer replacement algorithm using spatial and temporal locality
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
BRUST: an efficient buffer replacement for spatial databases
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
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Efficient spatial query processing is very important since the applications of the spatial DBMS (e.g. GIS, CAD/CAM, LBS) handle massive amount of data and consume much time. Many spatial queries contain the multi-way spatial join due to the fact that they compute the relationships (e.g. intersect) among the spatial data. Thus, accurate estimation of the spatial join selectivity is essential to generate an efficient spatial query execution plan that takes advantages of spatial access methods efficiently. For the multi-way spatial joins, the selectivity estimation formulae only for the two kinds of query types, tree and clique, have been developed. However, the selectivity estimation for the general query graph which contains cycles has not been developed yet. To fill this gap, we devise a formula for the multi-way spatial ring join selectivity. This is an indispensable step to compute the selectivity of the general multi-way spatial join whose join graph contains cycles. Our experiment shows that the estimated sizes of query results using our formula are close to the sizes of actual query results.