In-Memory Graph Databases for Web-Scale Data.

A software stack relies primarily on graph-based methods to implement scalable resource description framework databases on top of commodity clusters, providing an inexpensive way to extract meaning from volumes of heterogeneous data.

Detalles Bibliográficos
Publicado en:Computer (00189162) Vol. 48; no. 3; pp. 24 - 36
Autores principales: Castellana, Vito Giovanni, Morari, Alessandro, Weaver, Jesse, Tumeo, Antonino, Haglin, David, Villa, Oreste, Feo, John
Formato: Artículo
Publicado: IEEE Mar2015
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: In-Memory Graph Databases for Web-Scale Data.
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        au:
          Castellana, Vito Giovanni
          Morari, Alessandro
          Weaver, Jesse
          Tumeo, Antonino
          Haglin, David
          Villa, Oreste
          Feo, John
        affil:
          Pacific Northwest National Laboratory
          NVIDIA Research
          Context Relevant
      su:
        Computer software
        RDF (Document markup language)
        Databases
        Mass transfer coefficients
        Microclusters
      sug:
        subj:
          Computer software
          RDF (Document markup language)
          Databases
          Mass transfer coefficients
          Microclusters
      keyword:
        Algorithm design and analysis
        big data
        Clustering algorithms
        Data structures
        graph databases
        high-performance computing
        multithreading
        Pattern matching
        RDF databases
        Resource description framework
        Resource management
        Software development
        SPARQL
      ab: A software stack relies primarily on graph-based methods to implement scalable resource description framework databases on top of commodity clusters, providing an inexpensive way to extract meaning from volumes of heterogeneous data.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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