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Constellation Queries over Big Data

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  • معلومة اضافية
    • Contributors:
      Laboratorio Nacional de Computação Cientifica Rio de Janeiro (LNCC / MCT); Centro Federal de Educação Tecnológica Celso Suckow da Fonseca Rio de Janeiro ( CEFET/RJ); Scientific Data Management (ZENITH); Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM); Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM); Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria); Courant Institute of Mathematical Sciences New York (CIMS); New York University New York (NYU); NYU System (NYU)-NYU System (NYU); SBC; SciDISC Inria associated team with Brazil; European Project: 689772,H2020 Pilier Industrial Leadership,H2020-EUB-2015,HPC4E(2015)
    • بيانات النشر:
      HAL CCSD
      SBC
    • الموضوع:
      2018
    • Collection:
      Université de Montpellier: HAL
    • الموضوع:
    • نبذة مختصرة :
      International audience ; A geometrical pattern is a set of points with all pairwise distances (or, more generally, relative distances) specified. Finding matches to such patterns has applications to spatial data in seismic, astronomical, and transportation contexts. Finding geometric patterns is a challenging problem as the potential number of sets of elements that compose shapes is exponentially large in the size of the dataset and the pattern. In this paper, we propose algorithms to find patterns in large data applications. Our methods combine quadtrees, matrix multiplication, and bucket join processing to discover sets of points that match a geometric pattern within some additive factor on the pairwise distances. Our distributed experiments show that the choice of composition algorithm (matrix multiplication or nested loops) depends on the freedom introduced in the query geometry through the distance additive factor. Three clearly identified blocks of threshold values guide the choice of the best composition algorithm. ; Um padra ̃o geome ́trico e ́ definido por um conjunto de pontos e todos os pares de distaˆncias entre estes pontos. Encontrar casamentos de padro ̃es geome ́tricos em datasets tem aplicac ̧o ̃es na astronomia, na pesquisa s ́ısmica e no desenho de a ́reas urbanas. A soluc ̧a ̃o do problema impo ̃e um grande desafio, considerando-se o nu ́mero exponencial de candidatos, potencialmente func ̧a ̃o do nu ́mero de elementos no dataset e nu ́mero de pontos na forma geome ́trica. O me ́todo aqui apresentado inclui: quadtrees,multiplicac ̧a ̃o de matrizes e junc ̧o ̃es espaciais para encontrar conjuntos de pontos que se aproximem do padra ̃o fornecido, com um erro admiss ́ıvel. Apresentamos uma implementac ̧a ̃o dis- tribu ́ıda reveladora de que a escolha do algoritmo (multiplicac ̧a ̃o de matrizes ou junc ̧o ̃es espaciais) depende da liberdade introduzida por um fator de erro adi- tivo na geometria do padra ̃o. Identificamos treˆs regio ̃es baseadas nos valores de erro tolerados que determinam a ...
    • Relation:
      info:eu-repo/semantics/altIdentifier/arxiv/1703.02638; info:eu-repo/grantAgreement//689772/EU/HPC for Energy/HPC4E; lirmm-01867833; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01867833; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01867833/document; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01867833/file/085-sbbd_2018-fp.pdf; ARXIV: 1703.02638
    • الدخول الالكتروني :
      https://hal-lirmm.ccsd.cnrs.fr/lirmm-01867833
      https://hal-lirmm.ccsd.cnrs.fr/lirmm-01867833/document
      https://hal-lirmm.ccsd.cnrs.fr/lirmm-01867833/file/085-sbbd_2018-fp.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.49E49B82