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MeTree: A Metric Spatial Index

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Computer Science – CACIC 2019 (CACIC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1184))

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Abstract

Metric Spaces model databases allowing similarity searching. e.g., looking for objects similar to a given one. Spatial databases are used to store and efficiently retrieve data with some spatial attribute. Some applications need to search both by similarity and space at the same time. This kind of queries cannot be solved efficiently using spatial o metric indexes separately. Recently, Metric Spatial queries were formalized and a new access method, the MeTree, was proposed to solve them. In this article, we present new experiments that show the performance of this index.

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Authors and Affiliations

  1. UTN-FRCU, Concepción del Uruguay, Argentina

    Adrián Planas & Andrés Pascal

  2. UNSL-Departamento de Informática, San Luis, Argentina

    Norma Herrera

Authors
  1. Adrián Planas
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  2. Andrés Pascal
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  3. Norma Herrera
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Corresponding author

Correspondence to Andrés Pascal .

Editor information

Editors and Affiliations

  1. RedUNCI Chair, National University of La Plata, La Plata, Argentina

    Patricia Pesado

  2. National University of Río Cuarto, Río Cuarto, Argentina

    Marcelo Arroyo

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Planas, A., Pascal, A., Herrera, N. (2020). MeTree: A Metric Spatial Index. In: Pesado, P., Arroyo, M. (eds) Computer Science – CACIC 2019. CACIC 2019. Communications in Computer and Information Science, vol 1184. Springer, Cham. https://doi.org/10.1007/978-3-030-48325-8_17

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  • DOI: https://doi.org/10.1007/978-3-030-48325-8_17

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