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A Cost Model for Similarity Queries in Metric Spaces (1998)
| Citations: | 61 - 12 self |
BibTeX
@MISC{Ciaccia98acost,
author = {Paolo Ciaccia and Marco Patella and Pavel Zezula},
title = { A Cost Model for Similarity Queries in Metric Spaces },
year = {1998}
}
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Abstract
Wu consider tho problem of estimating CPU (distance com-putntlons) nnd I/O costs for processing range and k-nearest neighbors qucrics over metric spaces. Unlike the specific case of vector spaces, where information on data distribution has been exploited to derive cost models for predicting the per-formanco of multi-dimensional access methods, in a generic metric space there is no such a possibility, which makes the problem quite different and requires a novel approach. We insist that the distance distribution of objects can be prof-itably used to solve the problem, and consequently develop a concrete cost model for the M-tree access method [lo]. Our results rely on the assumption that the indexed dataset comes from a metric space which is “homogeneous ” enough (in a probabilistic sense) to allow reliable cost estimations even if the distance distribution with respect to a specific query object is unknown. We experimentally validate the modol ovor both real and synthetic datasets, and show how the model can be used to tune the M-tree in order to min-imlzo a combination of CPU and I/O costs. Finally, we sketch how the same approach can be applied to derive a cost model for the up-tree index structure [8].
Keyphrases
cost model metric space similarity query distance distribution vector space m-tree access method lo synthetic datasets specific case specific query object up-tree index structure generic metric space k-nearest neighbor qucrics novel approach reliable cost estimation multi-dimensional access method indexed dataset concrete cost model data distribution probabilistic sense tho problem
