M-tree: An Efficient Access Method for Similarity Search in Metric Spaces.

Paolo Ciaccia, Marco Patella, Pavel Zezula: M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. VLDB 1997: 426-435

@inproceedings{DBLP:conf/vldb/CiacciaPZ97,
  author    = {Paolo Ciaccia and
               Marco Patella and
               Pavel Zezula},
  editor    = {Matthias Jarke and
               Michael J. Carey and
               Klaus R. Dittrich and
               Frederick H. Lochovsky and
               Pericles Loucopoulos and
               Manfred A. Jeusfeld},
  title     = {M-tree: An Efficient Access Method for Similarity Search in Metric
               Spaces},
  booktitle = {VLDB'97, Proceedings of 23rd International Conference on Very
               Large Data Bases, August 25-29, 1997, Athens, Greece},
  publisher = {Morgan Kaufmann},
  year      = {1997},
  isbn      = {1-55860-470-7},
  pages     = {426-435},
  ee        = {db/conf/vldb/CiacciaPZ97.html},
  crossref  = {DBLP:conf/vldb/97},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

A new access method, called M-tree, is proposed to organize and search large data sets from a generic ``metric space", i.e. where object proximity is only defined by a distance function satisfying the positivity, symmetry, and triangle inequality postulates. We detail algorithms for insertion of objects and split management, which keep the M-tree always balanced - several heuristic split alternatives are considered and experimentally evaluated. Algorithms for similarity (range and k-nearest neighbors) queries are also described. Results from extensive experimentation with a prototype system are reported, considering as the performance criteria the number of page I/O's and the number of distance computations. The results demonstrate that the M-tree indeed extends the domain of applicability beyond the traditional vector spaces, performs reasonably well in high-dimensional data spaces, and scales well in case of growing files.

Copyright © 1997 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.

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