ACM SIGMOD Anthology VLDB dblp.uni-trier.de

The TV-Tree: An Index Structure for High-Dimensional Data.

King-Ip Lin, H. V. Jagadish, Christos Faloutsos: The TV-Tree: An Index Structure for High-Dimensional Data. VLDB J. 3(4): 517-542(1994)
@article{DBLP:journals/vldb/LinJF94,
  author    = {King-Ip Lin and
               H. V. Jagadish and
               Christos Faloutsos},
  title     = {The TV-Tree: An Index Structure for High-Dimensional Data},
  journal   = {VLDB J.},
  volume    = {3},
  number    = {4},
  year      = {1994},
  pages     = {517-542},
  ee        = {db/journals/vldb/LinJF94.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

We propose a file structure to index high-dimensionality data, which are typically points in some feature space. The idea is to use only a few of the features, using additional features only when the additional discriminatory power is absolutely necessary. We present in detail the design of our tree structure and the associated algorithms that handle such "varying length" feature vectors. Finallly, we report simulation results, comparing the proposed structure with the R*-tree, which is one of the most successful methods for low-dimensionality spaces. The results illustrate the superiority of our method, which saves up to 80% in disk accesses.

Copyright © 1994 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

Key Words

Spatial index, similarity retrieval, query by content.

Online Paper

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