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
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
Incremental distance join algorithms for spatial databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Adaptive multi-stage distance join processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Epsilon grid order: an algorithm for the similarity join on massive high-dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
High-Dimensional Similarity Joins
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
High Dimensional Similarity Joins: Algorithms and Performance Evaluation
ICDE '98 Proceedings of the Fourteenth 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 X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Partition-Based Similarity Join in High Dimensional Data Spaces
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
An empirical study on selective partitioning dimensions for partition-based similarity joins
Data & Knowledge Engineering
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Distributions of very high dimensional data are, in most cases, not even, but skewed. For this reason, there can be more effective dimensions than others in partitioning a high dimensional data set. Effective dimensions can be used to partition the data set in more balanced way so that data are located in more evenly distributed. In this paper, we present schemes to select dimensions by which high dimensional data sets are partitioned for efficient similarity joins. Especially, in order to efficiently reduce the number of partition dimensions, we propose a novel scheme using diagonal dimensions compared with perpendicular dimensions. The experimental results show that the proposed schemes substantially improve the performance of the partition-based similarity joins in high dimensional data spaces.