Slim-Trees: High Performance Metric Trees Minimizing Overlap between Nodes

  • Caetano TrainaJr.
  • Agma Traina
  • Bernhard Seeger
  • Christos Faloutsos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1777)


In this paper we present the Slim-tree, a dynamic tree for organizing metric datasets in pages of fixed size. The Slim-tree uses the “fat-factor” which provides a simple way to quantify the degree of overlap between the nodes in a metric tree. It is well-known that the degree of overlap directly affects the query performance of index structures. There are many suggestions to reduce overlap in multidimensional index structures, but the Slim-tree is the first metric structure explicitly designed to reduce the degree of overlap.

Moreover, we present new algorithms for inserting objects and splitting nodes. The new insertion algorithm leads to a tree with high storage utilization and improved query performance, whereas the new split algorithm runs considerably faster than previous ones, generally without sacrificing search performance. Results obtained from experiments with real-world data sets show that the new algorithms of the Slim-tree consistently lead to performance improvements. After performing the Slim-down algorithm, we observed improvements up to a factor of 35% for range queries.


Minimal Span Tree Index Structure Distance Calculation Range Query Point Query 
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 2000

Authors and Affiliations

  • Caetano TrainaJr.
    • 1
  • Agma Traina
    • 1
  • Bernhard Seeger
    • 2
  • Christos Faloutsos
    • 3
  1. 1.Department of Computer ScienceUniversity of São Paulo at São CarlosBrazil
  2. 2.Fachbereich Mathematik und InformatikUniversität MarburgGermany
  3. 3.Department of Computer ScienceCarnegie Mellon UniversityUSA

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