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
An algorithm for approximate closest-point queries
SCG '94 Proceedings of the tenth annual symposium on Computational geometry
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
SIGMOD '95 Proceedings of the 1995 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
Multidimensional access methods
ACM Computing Surveys (CSUR)
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Closest pair queries in spatial databases
SIGMOD '00 Proceedings of the 2000 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
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
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
A Cost Model and Index Architecture for the Similarity Join
Proceedings of the 17th International Conference on Data Engineering
Fast Nearest Neighbor Search in Medical Image Databases
VLDB '96 Proceedings of the 22th 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
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Multi-Way Distance Join Queries in Spatial Databases
Geoinformatica
Accelerating approximate similarity queries using genetic algorithms
Proceedings of the 2005 ACM symposium on Applied computing
A performance comparison of distance-based query algorithms using R-trees in spatial databases
Information Sciences: an International Journal
Approximate similarity search: A multi-faceted problem
Journal of Discrete Algorithms
Processing distance-based queries in multidimensional data spaces using R-trees
PCI'01 Proceedings of the 8th Panhellenic conference on Informatics
Approximate static and continuous range search in mobile navigation
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
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In modern database applications the similarity or dissimilarity of complex objects is examined by performing distance-based queries (DBQs) on data of high dimensionality. The R-tree and its variations are commonly cited multidimensional access methods that can be used for answering such queries. Although, the related algorithms work well for low-dimensional data spaces, their performance degrades as the number of dimensions increases (dimensionality curse). In order to obtain acceptable response time in high-dimensional data spaces, algorithms that obtain approximate solutions can be used. Three approximation techniques (驴-allowance, N-consider and M-consider) and the respective recursive branch-and-bound algorithms for DBQs are presented and studied in this paper. We investigate the performance of these algorithms for the most representative DBQs (the K-nearest neighbors query and the K-closest pairs query) in high-dimensional data spaces, where the point data sets are indexed by tree-like structures belonging to the R-tree family: R*- trees and X-trees. The searching strategy is tuned according to several parameters, in order to examine the trade-off between cost (I/O activity and response time) and accuracy of the result. The outcome of the experimental evaluation is the derivation of the outperforming DBQ approximate algorithm for large high-dimensional point data sets.