Data structures and algorithms 3: multi-dimensional searching and computational geometry
Data structures and algorithms 3: multi-dimensional searching and computational geometry
Computational geometry: an introduction
Computational geometry: an introduction
The input/output complexity of sorting and related problems
Communications of the ACM
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
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
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
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Multidimensional divide-and-conquer
Communications of the ACM
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 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
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
High-Dimensional Similarity Joins
ICDE '97 Proceedings of the Thirteenth 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
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th 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
An Algorithm for Computing the Overlay of k-Dimensional Spaces
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
XXL - A Library Approach to Supporting Efficient Implementations of Advanced Database Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Integrating similarity-based queries in image DBMSs
Proceedings of the 2004 ACM symposium on Applied computing
Hypercube sweeping algorithm for subsequence motion matching in large motion databases
Proceedings of the 2006 ACM international conference on Virtual reality continuum and its applications
Cost models for distance joins queries using R-trees
Data & Knowledge Engineering
Efficient index-based KNN join processing for high-dimensional data
Information and Software Technology
A performance comparison of distance-based query algorithms using R-trees in spatial databases
Information Sciences: an International Journal
Progressive merge join: a generic and non-blocking sort-based join algorithm
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A one-pass aggregation algorithm with the optimal buffer size in multidimensional OLAP
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Gorder: an efficient method for KNN join processing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
ACM Transactions on Database Systems (TODS)
Similarity joins as stronger metric operations
SIGSPATIAL Special
A disk-aware algorithm for time series motif discovery
Data Mining and Knowledge Discovery
Efficient exact edit similarity query processing with the asymmetric signature scheme
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
VA-files vs. r*-trees in distance join queries
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
Progressive high-dimensional similarity join
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Asymmetric signature schemes for efficient exact edit similarity query processing
ACM Transactions on Database Systems (TODS)
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Current data repositories include a variety of data types, including audio, images, and time series. State-of-the-art techniques for indexing such data and doing query processing rely on a transformation of data elements into points in a multidimensional feature space. Indexing and query processing then take place in the feature space. In this paper, we study algorithms for finding relationships among points in multidimensional feature spaces, specifically algorithms for multidimensional joins. Like joins of conventional relations, correlations between multidimensional feature spaces can offer valuable information about the data sets involved. We present several algorithmic paradigms for solving the multidimensional join problem and we discuss their features and limitations. We propose a generalization of the Size Separation Spatial Join algorithm, named Multidimensional Spatial Join (MSJ), to solve the multidimensional join problem. We evaluate MSJ along with several other specific algorithms, comparing their performance for various dimensionalities on both real and synthetic multidimensional data sets. Our experimental results indicate that MSJ, which is based on space filling curves, consistently yields good performance across a wide range of dimensionalities.