ACM Transactions on Database Systems (TODS)
A practical divide-and-conquer algorithm for the rectangle intersection problem
Information Sciences: an International Journal
Analysis of object oriented spatial access methods
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Redundancy in spatial databases
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
The design and analysis of spatial data structures
The design and analysis of spatial data structures
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
A comparison of spatial query processing techniques for native and parameter spaces
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
Spatial joins using seeded trees
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
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
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
Sampling from Spatial Databases
Proceedings of the Ninth International Conference on Data Engineering
Efficient Computation of Spatial Joins
Proceedings of the Ninth International Conference on Data Engineering
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
An Algorithm for Computing the Overlay of k-Dimensional Spaces
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
Generating Seeded Trees from Data Sets
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Caching Strategies for Spatial Joins
Geoinformatica
IEEE Transactions on Knowledge and Data Engineering
An Evaluation of Generic Bulk Loading Techniques
Proceedings of the 27th International Conference on Very Large Data Bases
The BASIS System: A Benchmarking Approach for Spatial Index Structures
STDBM '99 Proceedings of the International Workshop on Spatio-Temporal Database Management
A Performance Evaluation of Spatial Join Processing Strategies
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
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
Complex Spatial Query Processing
Geoinformatica
Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations
IEEE Transactions on Knowledge and Data Engineering
Tree-based partition querying: a methodology for computing medoids in large spatial datasets
The VLDB Journal — The International Journal on Very Large Data Bases
Solving similarity joins and range queries in metric spaces with the list of twin clusters
Journal of Discrete Algorithms
MOVIES: indexing moving objects by shooting index images
Geoinformatica
Medoid queries in large spatial databases
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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Existing methods for spatial joins require pre-existing spatial indices or other precomputation, but such approaches are inefficient and limited in generality. Operand data sets of spatial joins may not all have precomputed indices, particularly when they are dynamically generated by other selection or join operations. Also, existing spatial indices are mostly designed for spatial selections, and are not always efficient for joins. This paper explores the design and implementation of seeded trees [1], which are effective for spatial joins and efficient to construct at join time. Seeded trees are R-tree-like structures, but divided into seed levels and grown levels. This structure facilitates using information regarding the join to accelerate the join process, and allows efficient buffer management. In addition to the basic structure and behavior of seeded trees, we present techniques for efficient seeded tree construction, a new buffer management strategy to lower I/O costs, and theoretical analysis for choosing algorithmic parameters. We also present methods for reducing space requirements and improving the stability of seeded tree performance with no additional I/O costs. Our performance studies show that the seeded tree method outperforms other tree-based methods by far both in terms of the number disk pages accessed and weighted I/O costs. Further, its performance gain is stable across different input data, and its incurred CPU penalties are also lower.