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
The hB-tree: a multiattribute indexing method with good guaranteed performance
ACM Transactions on Database Systems (TODS)
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
Fast Indexing and Visualization of Metric Data Sets using Slim-Trees
IEEE Transactions on Knowledge and Data Engineering
Improving the Performance of Multi-Dimensional Access Structures Based on k-d-Trees
ICDE '96 Proceedings of the Twelfth International Conference on 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
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An Optimal Algorithm for Approximating a Set of Rectangles by Two Minimum Area Rectangles
CG '91 Proceedings of the International Workshop on Computational Geometry - Methods, Algorithms and Applications
M+-tree: a new dynamical multidimensional index for metric spaces
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Principles and applications for supporting similarity queries in non-ordered-discrete and continuous data spaces
ACM Transactions on Database Systems (TODS)
The ND-tree: a dynamic indexing technique for multidimensional non-ordered discrete data spaces
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
ACM Transactions on Database Systems (TODS)
Journal of Information Science
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
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There is an increasing demand for similarity searches in a multidimensional non-ordered discrete data space (NDDS) from application areas such as bioinformatics and data mining. The non-ordered and discrete nature of an NDDS raises new challenges for developing efficient indexing methods for similarity searches. In this article, we propose a new indexing technique, called the NSP-tree, to support efficient similarity searches in an NDDS. As we know, overlap causes a performance degradation for indexing methods (e.g., the R-tree) for a continuous data space. In an NDDS, this problem is even worse due to the limited number of elements available on each dimension of an NDDS. The key idea of the NSP-tree is to use a novel discrete space-partitioning (SP) scheme to ensure no overlap at each level in the tree. A number of heuristics and strategies are incorporated into the tree construction algorithms to deal with the challenges for developing an SP-based index tree for an NDDS. Our experiments demonstrate that the NSP-tree is quite promising in supporting efficient similarity searches in NDDSs. We have compared the NSP-tree with the ND-tree, a data-partitioning-based indexing technique for NDDSs that was proposed recently, and the linear scan using different NDDSs. It was found that the search performance of the NSP-tree was better than those of both methods.