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PODS

1998

ACM

1998

ACM

We consider the problem of estimating CPU (distance computations) and I/O costs for processing range and k-nearest neighbors queries 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 performance 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 profitably used to solve the problem, and consequently develop a concrete cost model for the M-tree access method [10]. 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 model over both real and synthetic datasets, ...

Related Content

Added |
05 Aug 2010 |

Updated |
05 Aug 2010 |

Type |
Conference |

Year |
1998 |

Where |
PODS |

Authors |
Paolo Ciaccia, Marco Patella, Pavel Zezula |

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