Optimizing the spatial approximation tree from the root

• Main Authors: Alejandro J. Gómez, Verónica Ludueña, Nora Susana Reyes
• Format: Article
• Language: English
• Published: Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata 2008-07-01
• Series: Journal of Computer Science and Technology
• Subjects: similarity search; metric spaces; databases
• Online Access: https://journal.info.unlp.edu.ar/JCST/article/view/750

Many computational applications need to look for information in a database. Nowadays, the predominance of nonconventional databases makes the similarity search (i.e., searching elements of the database that are "similar" to a given query) becomes a preponderant concept. The Spatial Approximation Tree has been shown that it compares favorably against alternative data structures for similarity searching in metric spaces of medium to high dimensionality ("difficult" spaces) or queries with low selectivity. However, for the construction process the tree root has been randomly selected and the tree ,in its shape and performance, is completely determined by this selection. Therefore, we are interested in improve mainly the searches in this data structure trying to select the tree root so to reflect some of the own characteristics of the metric space to be indexed. We regard that selecting the root in this way it allows a better adaption of the data structure to the intrinsic dimensionality of the metric space considered, so also it achieves more efficient similarity searches.