An Indexing Structure for Dynamic Multidimensional Data in Vector Space

  • Elena Mikhaylova
  • Boris Novikov
  • Anton Volokhov
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 186)


The multidimensional kNN (k nearest neighbors) query problem is relevant to a large variety of database applications, including information retrieval, natural language processing, and data mining. To solve it efficiently, the database needs an indexing structure that provides this kind of search. However, attempts to find an exact solution are hardly feasible in multidimensional space. In this paper, a novel indexing technique for the approximate solution of kNN problem is described and analyzed. The construction of the indexing tree is based on clustering. Indexing structure is implemented on top of high-performance industrial DBMS.


Leaf Node Voronoi Diagram Subspace Cluster Initial Dataset Tree Edit 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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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Elena Mikhaylova
    • 1
  • Boris Novikov
    • 1
  • Anton Volokhov
    • 1
  1. 1.Saint Petersburg UniversityPetersburgRussia

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