RIVA: Indexing and Visualization of High-Dimensional Data Via Dimension Reorderings

  • Michail Vlachos
  • Spiros Papadimitriou
  • Zografoula Vagena
  • Philip S. Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


We propose a new representation for high-dimensional data that can prove very effective for visualization, nearest neighbor (NN) and range searches. It has been unequivocally demonstrated that existing index structures cannot facilitate efficient search in high-dimensional spaces. We show that a transformation from points to sequences can potentially diminish the negative effects of the dimensionality curse, permitting an efficient NN-search. The transformed sequences are optimally reordered, segmented and stored in a low-dimensional index. The experimental results validate that the proposed representation can be a useful tool for the fast analysis and visualization of high-dimensional databases.


Travel Salesman Problem Near Neighbor Query Point Projected Dimension Dimension Distance 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michail Vlachos
    • 1
  • Spiros Papadimitriou
    • 1
  • Zografoula Vagena
    • 2
  • Philip S. Yu
    • 1
  1. 1.IBM T.J. Watson Research CenterHawthorneUSA
  2. 2.IBM Almaden Research CenterSan JoseUSA

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