Abstract
Dynamic spatial approximation trees (dsa-trees) are efficient data structures for searching metric spaces. However, using enough storage, pivoting schemes beat dsa-trees in any metric space. In this paper we combine both concepts in a data structure that enjoys the features of dsa-trees and that improves query time by making the best use of the available memory. We show experimentally that our data structure is competitive for searching metric spaces. © Springer-Verlag Berlin Heidelberg 2003.
Cite
CITATION STYLE
Arroyuelo, D., Muñoz, F., Navarro, G., & Reyes, N. (2003). Memory-adaptative dynamic spatial approximation trees. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2857, 360–368. https://doi.org/10.1007/978-3-540-39984-1_28
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