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Spatial Indexing for Scalability in FCA

  • Ben Martin
  • Peter Eklund
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3874)

Abstract

The paper provides evidence that spatial indexing structures offer faster resolution of Formal Concept Analysis queries than B-Tree/Hash methods. We show that many Formal Concept Analysis operations, computing the contingent and extent sizes as well as listing the matching objects, enjoy improved performance with the use of spatial indexing structures such as the RD-Tree. Speed improvements can vary up to eighty times faster depending on the data and query. The motivation for our study is the application of Formal Concept Analysis to Semantic File Systems. In such applications millions of formal objects must be dealt with. It has been found that spatial indexing also provides an effective indexing technique for more general purpose applications requiring scalability in Formal Concept Analysis systems. The coverage and benchmarking are presented with general applications in mind.

Keywords

Formal Context Formal Concept Analysis Spatial Indexing Query Object Base Table 
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

  • Ben Martin
    • 1
  • Peter Eklund
    • 2
  1. 1.Information Technology and Electrical EngineeringThe University of QueenslandSt. LuciaAustralia
  2. 2.School of Economics and Information SystemsThe University of WollongongWollongongAustralia

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