Exploring the Potential of k^n-Tree for Efficient Representation of n-ary Relations

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Sebastián Alexis Moraga

Abstract

The objective of this experimental study was to investigate the scalability of gif.latex?k^n-trees, a compact data structure designed for representing gif.latex?n-ary relations, compared to a baseline based on a plain representation of adjacency lists. A literature review of compact data structures was conducted, focusing on gif.latex?k^n-trees and their potential for efficient gif.latex?n -ary data representation. To assess scalability, experiments comparing gif.latex?k^n-tree performance against the baseline using set intersection as a benchmark were conducted. Results demonstrated superior gif.latex?k^n-tree scalability in terms of time and memory, especially for high-dimensional and clustered datasets. On average, gif.latex?k^n-trees were eight times faster and consumed  times less memory than the baseline. The study also analyzed the impact of the order parameter gif.latex?k^n on performance, revealing a trade-off between space efficiency and query time. This study provides valuable insights into the practical applicability of gif.latex?k^n-trees for managing and querying high-dimensional data.

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Research Articles

References

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