Multiattribute hashing using Gray codes
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Spatial query processing in an object-oriented database system
SIGMOD '86 Proceedings of the 1986 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
Linear clustering of objects with multiple attributes
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
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
Multidimensional access methods
ACM Computing Surveys (CSUR)
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Distance browsing in spatial databases
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
Spatial Join Processing Using Corner Transformation
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases
Proceedings of the Fifth 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
A Region Splitting Strategy for Physical Database Design of Multidimensional File Organizations
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
The Transformation Technique for Spatial Objects Revisited
SSD '93 Proceedings of the Third International Symposium on Advances in Spatial Databases
Proceedings of the Sixth International Conference on Data Engineering
Techniques for Design and Implementation of Efficient Spatial Access Methods
VLDB '88 Proceedings of the 14th International Conference on Very Large Data Bases
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
ACM Transactions on Database Systems (TODS)
Journal of Systems and Software
Locality of Corner Transformation for Multidimensional Spatial Access Methods
Electronic Notes in Theoretical Computer Science (ENTCS)
Efficient processing of spatial joins with DOT-based indexing
Information Sciences: an International Journal
Top-k similarity join over multi-valued objects
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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Spatial joins find all pairs of objects that satisfy a given spatial relationship. In spatial joins using indexes, original-space indexes such as the R-tree are widely used. An original-space index is the one that indexes objects as represented in the original space. Since original-space indexes deal with extents of objects, it is relatively complex to optimize join algorithms using these indexes. On the other hand, transform-space indexes, which transform objects in the original space into points in the transform space and index them, deal only with points but no extents. Thus, optimization of join algorithms using these indexes can be relatively simple. However, the disadvantage of these join algorithms is that they cannot be applied to original-space indexes such as the R-tree. In this paper, we present a novel mechanism for achieving the best of these two types of algorithms. Specifically, we propose the new notion of the transform-space view and present the transform-space view join algorithm. The transform-space view is a virtual transform-space index based on an original-space index. It allows us to "interpret” or "view” an existing original-space index as a transform-space index with no space and negligible time overhead and without actually modifying the structure of the original-space index or changing object representation. The transform-space view join algorithm joins two original-space indexes in the transform space through the notion of the transform-space view. Through analysis and experiments, we verify the excellence of the transform-space view join algorithm. The transform-space view join algorithm always outperforms existing ones for all the data sets tested in terms of all three measures used: the one-pass buffer size (the minimum buffer size required for guaranteeing one disk access per page), the number of disk accesses for a given buffer size, and the wall clock time. Thus, it constitutes a lower-bound algorithm. We believe that the proposed transform-space view can be applied to developing various new spatial query processing algorithms in the transform space.