Spatial selection of sparse pivots for similarity search in metric spaces


  • Nieves Rodríguez Brisaboa Database Laboratory, University of A Coruña, Campus de Elviña s/n, A Coruña, 15071, Spain
  • Antonio Fariña Database Laboratory, University of A Coruña, Campus de Elviña s/n, A Coruña, 15071, Spain
  • Óscar Pedreira Database Laboratory, University of A Coruña, Campus de Elviña s/n, A Coruña, 15071, Spain
  • Nora Susana Reyes Departamento de Informática, Universidad Nacional de San Luis, San Luis, Argentina


Similarity Search, metric spaces, pivot selection, databases, searching, indexing


Similarity search is a fundamental operation for applications that deal with unstructured data sources. In this paper we propose a new pivot-based method for similarity search, called Sparse Spatial Selection (SSS). The main characteristic of this method is that it guarantees a good pivot selection more efficiently than other methods previously proposed. In addition, SSS adapts itself to the dimensionality of the metric space we are working with, without being necessary to specify in advance the number of pivots to use. Furthermore, SSS is dynamic, that is, it is capable to support object insertions in the database efficiently, it can work with both continuous and discrete distance functions, and it is suitable for secondary memory storage. In this work we provide experimental results that confirm the advantages of the method with several vector and metric spaces. We also show that the efficiency of our proposal is similar to that of other existing ones over vector spaces, although it is better over general metric spaces.


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How to Cite

Rodríguez Brisaboa, N., Fariña, A., Pedreira, Óscar, & Reyes, N. S. (2007). Spatial selection of sparse pivots for similarity search in metric spaces. Journal of Computer Science and Technology, 7(01), p. 8–13. Retrieved from



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