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
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The string B-tree: a new data structure for string search in external memory and its applications
Journal of the ACM (JACM)
ACM Transactions on Database Systems (TODS)
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
Indexing and Retrieval for Genomic Databases
IEEE Transactions on Knowledge and Data Engineering
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
The LSDh-Tree: An Access Structure for Feature Vectors
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
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
Efficient querying on genomic databases by using metric space indexing techniques
DEXA '97 Proceedings of the 8th International Workshop on Database and Expert Systems Applications
The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient similarity search based on data distribution properties in high dimensions
Efficient similarity search based on data distribution properties in high dimensions
A space-partitioning-based indexing method for multidimensional non-ordered discrete data spaces
ACM Transactions on Information Systems (TOIS)
Space-Partitioning-Based Bulk-Loading for the NSP-Tree in Non-ordered Discrete Data Spaces
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
The C-ND tree: a multidimensional index for hybrid continuous and non-ordered discrete data spaces
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Efficient k-nearest neighbor searching in nonordered discrete data spaces
ACM Transactions on Information Systems (TOIS)
Bulk-loading the ND-tree in non-ordered discrete data spaces
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Indexing structures for content-based retrieval of large image databases: a review
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Generalizing the k-Windows clustering algorithm in metric spaces
Mathematical and Computer Modelling: An International Journal
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Similarity searches in multidimensional Nonordered Discrete Data Spaces (NDDS) are becoming increasingly important for application areas such as genome sequence databases. Existing indexing methods developed for multidimensional (ordered) Continuous Data Spaces (CDS) such as R-tree cannot be directly applied to an NDDS. This is because some essential geometric concepts/properties such as the minimum bounding region and the area of a region in a CDS are no longer valid in an NDDS. On the other hand, indexing methods based on metric spaces such as M-tree are too general to effectively utilize the data distribution characteristics in an NDDS. Therefore, their retrieval performance is not optimized. To support efficient similarity searches in an NDDS, we propose a new dynamic indexing technique, called the ND-tree. The key idea is to extend the relevant geometric concepts as well as some indexing strategies used in CDSs to NDDSs. Efficient algorithms for ND-tree construction are presented. Our experimental results on synthetic and genomic sequence data demonstrate that the performance of the ND-tree is significantly better than that of the linear scan and M-tree in high dimensional NDDSs.