The Omni-family of all-purpose access methods: a simple and effective way to make similarity search more efficient

The Omni-family of all-purpose access methods: a simple and effective way to make similarity... Similarity search operations require executing expensive algorithms, and although broadly useful in many new applications, they rely on specific structures not yet supported by commercial DBMS. In this paper we discuss the new Omni-technique, which allows to build a variety of dynamic Metric Access Methods based on a number of selected objects from the dataset, used as global reference objects. We call them as the Omni-family of metric access methods. This technique enables building similarity search operations on top of existing structures, significantly improving their performance, regarding the number of disk access and distance calculations. Additionally, our methods scale up well, exhibiting sub-linear behavior with growing database size. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

The Omni-family of all-purpose access methods: a simple and effective way to make similarity search more efficient

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Publisher
Springer-Verlag
Copyright
Copyright © 2007 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-005-0178-0
Publisher site
See Article on Publisher Site

Abstract

Similarity search operations require executing expensive algorithms, and although broadly useful in many new applications, they rely on specific structures not yet supported by commercial DBMS. In this paper we discuss the new Omni-technique, which allows to build a variety of dynamic Metric Access Methods based on a number of selected objects from the dataset, used as global reference objects. We call them as the Omni-family of metric access methods. This technique enables building similarity search operations on top of existing structures, significantly improving their performance, regarding the number of disk access and distance calculations. Additionally, our methods scale up well, exhibiting sub-linear behavior with growing database size.

Journal

The VLDB JournalSpringer Journals

Published: Oct 1, 2007

References

  • Indexing large metric spaces for similarity search queries
    Bozkaya, T.; Ózsoyoglu, Z. Meral.
  • Intrinsic dimension estimation of data: an approach based on Grassberger-Procaccia's algorithm
    Camastra, F.; Vinciarelli, A.

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