Spatial query processing in an object-oriented database system
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Analysis of object oriented spatial access methods
SIGMOD '87 Proceedings of the 1987 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
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
The SEQUOIA 2000 storage benchmark
SIGMOD '93 Proceedings of the 1993 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
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
Spatial joins using seeded trees
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
The Montage extensible DataBlade architecture
SIGMOD '94 Proceedings of the 1994 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
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
The Grid File: An Adaptable, Symmetric Multikey File Structure
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
The Implementation of POSTGRES
IEEE Transactions on Knowledge and Data Engineering
Integrated Query Processing Strategies for Spatial Path Queries
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Proceedings of the Seventh International Conference on Data Engineering
Distance-Associated Join Indices for Spatial Range Search
Proceedings of the Eighth International Conference on Data Engineering
Efficient Computation of Spatial Joins
Proceedings of the Ninth International Conference on Data Engineering
Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Improving Spatial Intersect Joins Using Symbolic Intersect Detection
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Incremental distance join algorithms for spatial databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Bulk-insertions into R-trees using the small-tree-large-tree approach
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
Integration of spatial join algorithms for processing multiple inputs
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Transactions on Database Systems (TODS)
Cost models for overlapping and multiversion structures
ACM Transactions on Database Systems (TODS)
Efficient Cost Models for Spatial Queries Using R-Trees
IEEE Transactions on Knowledge and Data Engineering
Heuristic approach for early separated filter and refinement strategy in spatial query optimization
Journal of Systems and Software
IEEE Transactions on Knowledge and Data Engineering
Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
GBI: A Generalized R-Tree Bulk-Insertion Strategy
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Analysis of predictive spatio-temporal queries
ACM Transactions on Database Systems (TODS)
Approximation techniques for spatial data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Cost models for distance joins queries using R-trees
Data & Knowledge Engineering
Query optimizer for spatial join operations
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
ACM Transactions on Database Systems (TODS)
Multi-way spatial join selectivity for the ring join graph
Information and Software Technology
On estimating result sizes of multi-way spatial joins
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartIII
The k closest pairs in spatial databases
Geoinformatica
Hi-index | 0.00 |
The development of a cost model for predicting the performance of spatial joins has been identified in the literature as an important and difficult problem. In this paper, we present the first cost model that can predict the performance of spatial joins using R-trees. Based on two existing R-trees (join targets), our model first estimates the number of expected I/Os for the join process by assuming a zero buffer size. Our method for this estimation extends the cost model for R-tree window queries (developed by Kamel and Faloutsos and by Pagel et al.) to also handle spatial joins (which are more complex). In the context of spatial join processing, this number of zero-buffer expected I/Os is not practical for performance prediction in a buffered environment. To model the buffer impact, we use an (exponential) distribution function to measure the probability that a buffer-less I/O would cause a page fault in a buffered environment. Based on this probability and the zero-buffer expected I/O cost, the estimated number of I/Os for an R-tree join can then be computed. The comparisons between the predictions from our cost model and the actual results from our experiments based on real GIS maps show that the average relative error ratio is about 10% with a maximum of about 20% for a wide range of buffer sizes. Therefore, our model is a useful tool for the query optimization of spatial join queries.