The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 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
Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
ACM Computing Surveys (CSUR)
Slim-Trees: High Performance Metric Trees Minimizing Overlap Between Nodes
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
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
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th 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
D-Index: Distance Searching Index for Metric Data Sets
Multimedia Tools and Applications
M-Grid: similarity searching in grid
P2PIR '06 Proceedings of the international workshop on Information retrieval in peer-to-peer networks
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
Dynamic Spatial Approximation Trees for Massive Data
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
DSACL+-tree: a dynamic data structure for similarity search in secondary memory
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
| Hi-index | 0.00 |
Similarity retrieval is an important paradigm for searching in environments where exact match has little meaning Moreover, in order to enlarge the set of data types for which the similarity search can efficiently be performed, the mathematical notion of metric space provides a useful abstraction of similarity In this paper, we present a novel access structure for similarity search in arbitrary metric spaces, called D-Index D-Index supports easy insertions and deletions and bounded search costs for range queries with radius up to ρ D-Index also supports disk memories, thus, it is able to deal with large archives However, the partitioning principles employed in the D-Index are not very optimal since they produce high number of empty partitions We propose several strategies of partitioning and, finally, compare them.