VA-Files vs. R*-Trees in Distance Join Queries

  • Antonio Corral
  • Alejandro D’Ermiliis
  • Yannis Manolopoulos
  • Michael Vassilakopoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3631)


In modern database applications the similarity of complex objects is examined by performing distance-based queries (e.g. nearest neighbour search) on data of high dimensionality. Most multidimensional indexing methods have failed to efficiently support these queries in arbitrary high-dimensional datasets (due to the dimensionality curse). Similarity join queries and K closest pairs queries are the most representative distance join queries, where two high-dimensional datasets are combined. These queries are very expensive in terms of response time and I/O activity in case of high-dimensional spaces. On the other hand, the filtering-based approach, as applied by the VA-file, has turned out to be a very promising alternative for nearest neighbour search. In general, the filtering-based approach represents vectors as compact approximations, whereas by first scanning these approximations, only a small fraction of the real vectors is visited. Here, we elaborate on VA-files and develop VA-file based algorithms for answering similarity join and K closest pairs queries on high-dimensional data. Also, performance-wise we compare the use of VA-files and R*-trees (a structure that has been proven to be of robust nature) for answering these queries. The results of the comparison do not lead to a clear winner.


Close Pair Similarity Join Page Access Vector File Compact Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Antonio Corral
    • 1
  • Alejandro D’Ermiliis
    • 1
  • Yannis Manolopoulos
    • 2
  • Michael Vassilakopoulos
    • 3
  1. 1.Department of Languages and ComputingUniversity of AlmeriaAlmeriaSpain
  2. 2.Department of InformaticsAristotle UniversityThessalonikiGreece
  3. 3.Department of InformaticsTechnological Educational Institute of ThessalonikiThessalonikiGreece

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