Computational geometry: an introduction
Computational geometry: an introduction
Space-economical plane-sweep algorithms
Computer Vision, Graphics, and Image Processing
Spatial query processing in an object-oriented database system
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
A practical divide-and-conquer algorithm for the rectangle intersection problem
Information Sciences: an International Journal
GENESIS: An Extensible Database Management System
IEEE Transactions on Software Engineering
Introduction to algorithms
Strategies for optimizing the use of redundancy in spatial databases
SSD '90 Proceedings of the first symposium on Design and implementation of large spatial databases
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
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Hashing by proximity to process duplicates in spatial databases
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Spatial joins using seeded trees
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
GENESYS: a system for efficient spatial query processing
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
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Integration of spatial join algorithms for processing multiple inputs
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Vector approximation based indexing for non-uniform high dimensional data sets
Proceedings of the ninth international conference on Information and knowledge management
Data Structures for Range Searching
ACM Computing Surveys (CSUR)
A data structure for manipulating priority queues
Communications of the ACM
Improving the Query Performance of High-Dimensional Index Structures by Bulk-Load Operations
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Efficient Computation of Spatial Joins
Proceedings of the Ninth International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Scalable Sweeping-Based Spatial Join
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Object and File Management in the EXODUS Extensible Database System
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
Benchmarking Spatial Join Operations with Spatial Output
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Singularities Make Spatial Join Scheduling Hard
ISAAC '97 Proceedings of the 8th International Symposium on Algorithms and Computation
An Algorithm for Computing the Overlay of k-Dimensional Spaces
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
SSD '93 Proceedings of the Third 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
Improving Spatial Intersect Joins Using Symbolic Intersect Detection
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Space efficient algorithms for VLSI artwork analysis
DAC '83 Proceedings of the 20th Design Automation Conference
Data Redundancy and Duplicate Detection in Spatial Join Processing
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
I/O Efficient Scientific Computation Using TPIE
I/O Efficient Scientific Computation Using TPIE
A spatial hash join algorithm suited for small buffer size
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Transform-Space View: Performing Spatial Join in the Transform Space Using Original-Space Indexes
IEEE Transactions on Knowledge and Data 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)
ACM Transactions on Database Systems (TODS)
Predictive Join Processing between Regions and Moving Objects
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
Stepwise optimization method for k-CNN search for location-based service
SOFSEM'05 Proceedings of the 31st international conference on Theory and Practice of Computer Science
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The key issue in performing spatial joins is finding the pairs of intersecting rectangles. For unindexed data sets, this is usually resolved by partitioning the data and then performing a plane sweep on the individual partitions. The resulting join can be viewed as a two-step process where the partition corresponds to a hash-based join while the plane-sweep corresponds to a sort-merge join. In this article, we look at extending the idea of the sort-merge join for one-dimensional data to multiple dimensions and introduce the Iterative Spatial Join. As with the sort-merge join, the Iterative Spatial Join is best suited to cases where the data is already sorted. However, as we show in the experiments, the Iterative Spatial Join performs well when internal memory is limited, compared to the partitioning methods. This suggests that the Iterative Spatial Join would be useful for very large data sets or in situations where internal memory is a shared resource and is therefore limited, such as with today's database engines which share internal memory amongst several queries. Furthermore, the performance of the Iterative Spatial Join is predictable and has no parameters which need to be tuned, unlike other algorithms. The Iterative Spatial Join is based on a plane sweep algorithm, which requires the entire data set to fit in internal memory. When internal memory overflows, the Iterative Spatial Join simply makes additional passes on the data, thereby exhibiting only a gradual performance degradation. To demonstrate the use and efficacy of the Iterative Spatial Join, we first examine and analyze current approaches to performing spatial joins, and then give a detailed analysis of the Iterative Spatial Join as well as present the results of extensive testing of the algorithm, including a comparison with partitioning-based spatial join methods. These tests show that the Iterative Spatial Join overcomes the performance limitations of the other algorithms for data sets of all sizes as well as differing amounts of internal memory.