Subspace Similarity Search: Efficient k-NN Queries in Arbitrary Subspaces

  • Thomas Bernecker
  • Tobias Emrich
  • Franz Graf
  • Hans-Peter Kriegel
  • Peer Kröger
  • Matthias Renz
  • Erich Schubert
  • Arthur Zimek
Conference paper

DOI: 10.1007/978-3-642-13818-8_38

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6187)
Cite this paper as:
Bernecker T. et al. (2010) Subspace Similarity Search: Efficient k-NN Queries in Arbitrary Subspaces. In: Gertz M., Ludäscher B. (eds) Scientific and Statistical Database Management. SSDBM 2010. Lecture Notes in Computer Science, vol 6187. Springer, Berlin, Heidelberg

Abstract

There are abundant scenarios for applications of similarity search in databases where the similarity of objects is defined for a subset of attributes, i.e., in a subspace, only. While much research has been done in efficient support of single column similarity queries or of similarity queries in the full space, scarcely any support of similarity search in subspaces has been provided so far. The three existing approaches are variations of the sequential scan. Here, we propose the first index-based solution to subspace similarity search in arbitrary subspaces.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Thomas Bernecker
    • 1
  • Tobias Emrich
    • 1
  • Franz Graf
    • 1
  • Hans-Peter Kriegel
    • 1
  • Peer Kröger
    • 1
  • Matthias Renz
    • 1
  • Erich Schubert
    • 1
  • Arthur Zimek
    • 1
  1. 1.Institut für InformatikLudwig-Maximilians-Universität München 

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