Incremental Attribute Computation in Component-Hypertrees

  • Alexandre MorimitsuEmail author
  • Wonder Alexandre Luz Alves
  • Dennis José da Silva
  • Charles Ferreira Gobber
  • Ronaldo Fumio HashimotoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11564)


Component-hypertrees are structures that store nodes of multiple component trees built with increasing neighborhoods, meaning they retain the same desirable properties of component trees but also store nodes from multiple scales, at the cost of increasing time and memory consumption for building, storing and processing the structure. In recent years, algorithmic advances resulted in optimization for both building and storing hypertrees. In this paper, we intend to further extend advances in this field, by presenting algorithms for efficient attribute computation and statistical measures that analyze how attribute values vary when nodes are merged in bigger scales. To validate the efficiency of our method, we present complexity and time consumption analyses, as well as a simple application to show the usefulness of the statistical measurements.


Component tree Component-hypertree Connected operators Attribute computation 



This study was financed in part by the CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance Code 001); FAPESP - Fundação de Amparo a Pesquisa do Estado de São Paulo (Proc. 2015/01587-0 and 2018/15652-7); CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico (Proc. 428720/2018-8).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexandre Morimitsu
    • 1
    Email author
  • Wonder Alexandre Luz Alves
    • 2
  • Dennis José da Silva
    • 1
  • Charles Ferreira Gobber
    • 2
  • Ronaldo Fumio Hashimoto
    • 1
    Email author
  1. 1.Instituto de Matemática e EstatísticaUniversidade de São PauloSão PauloBrazil
  2. 2.Universidade Nove de JulhoSão PauloBrazil

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