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

  • Authors:

  • Caetano Traina, Jr.;Roberto F. Filho;Agma J. Traina;Marcos R. Vieira;Christos Faloutsos

  • Affiliations:

  • Department of Computer Science, Drop and Statistics, University of São Paulo at São Carlos, São Carlos, Brazil;Department of Computer Science, Drop and Statistics, University of São Paulo at São Carlos, São Carlos, Brazil;Department of Computer Science, Drop and Statistics, University of São Paulo at São Carlos, São Carlos, Brazil;Department of Computer Science, Drop and Statistics, University of São Paulo at São Carlos, São Carlos, Brazil;Department of Computer Science, Carnegie Mellon University, Pittsburgh, USA

  • Venue:
  • The VLDB Journal — The International Journal on Very Large Data Bases
  • Year:
  • 2007

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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.