Metric Indexing Assisted by Short-Term Memories

  • Humberto RazenteEmail author
  • Régis Michel Santos Sousa
  • Maria Camila Nardini Barioni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11223)


Similarity queries are fundamental operations for applications that deal with complex data. This work proposes a new approach, called MIA (Metric Indexing Assisted by auxiliary memory with limited capacity), that can be employed to create dynamic metric access methods, such as M-trees and Slim-trees, through a short-term memory. We propose three strategies that were evaluated with various datasets and employing different node split policies. Experimental results show that metric access methods built with the MIA approach present better distribution of the elements in the index nodes when compared to the access methods built without it. Moreover, these results show the strategies decrease the overlap, the number of distance calculations, the number of disk accesses and the execution time to run k-nearest neighbor queries.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculdade de Computação (FACOM)Universidade Federal de Uberlândia (UFU)UberlândiaBrazil

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