| AMATO, Giuseppe, Fausto RABITTI, Pasquale SAVINO and Pavel ZEZULA. Region proximity in metric spaces and its use for approximate similarity search. ACM Transactions on Information Systems. New York: ACM Press, vol. 21, No 2, p. 192 - 227. ISSN 1046-8188. 2003. |
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@article{486237,
author = {Amato, Giuseppe and Rabitti, Fausto and Savino, Pasquale and Zezula, Pavel},
article_location = {New York},
article_number = {2},
keywords = {Approximation algorithms; approximate similarity search; metric data; metric trees; performance evaluation},
language = {eng},
issn = {1046-8188},
journal = {ACM Transactions on Information Systems},
title = {Region proximity in metric spaces and its use for approximate similarity search},
url = {http://doi.acm.org/10.1145/763693.763696},
volume = {21},
year = {2003}
}
TY - JOUR
ID - 486237
AU - Amato, Giuseppe - Rabitti, Fausto - Savino, Pasquale - Zezula, Pavel
PY - 2003
TI - Region proximity in metric spaces and its use for approximate similarity search
JF - ACM Transactions on Information Systems
VL - 21
IS - 2
SP - 192 - 227
EP - 192 - 227
PB - ACM Press
SN - 10468188
KW - Approximation algorithms
KW - approximate similarity search
KW - metric data
KW - metric trees
KW - performance evaluation
UR - http://doi.acm.org/10.1145/763693.763696
N2 - Similarity search structures for metric data typically bound object partitions by ball regions. Since regions can overlap, a relevant issue is to estimate the proximity of regions in order to predict the number of objects in the regions' intersection. This paper analyzes the problem using a probabilistic approach and provides a solution that effectively computes the proximity through realistic heuristics that only require small amounts of auxiliary data. An extensive simulation to validate the technique is provided. An application is developed to demonstrate how the proximity measure can be successfully applied to the approximate similarity search. Search speedup is achieved by ignoring data regions whose proximity to the query region is smaller than a user-defined threshold. This idea is implemented in a metric tree environment for the similarity range and "nearest neighbors" queries. Several measures of efficiency and effectiveness are applied to evaluate proposed approximate search algorithms on real-life data sets. An analytical model is developed to relate proximity parameters and the quality of search. Improvements of two orders of magnitude are achieved for moderately approximated search results. We demonstrate that the precision of proximity measures can significantly influence the quality of approximated algorithms.
ER -
AMATO, Giuseppe, Fausto RABITTI, Pasquale SAVINO and Pavel ZEZULA. Region proximity in metric spaces and its use for approximate similarity search. \textit{ACM Transactions on Information Systems}. New York: ACM Press, vol.~21, No~2, p.~192 - 227. ISSN~1046-8188. 2003.
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