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Graph Representations and Storage

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Encyclopedia of Big Data Technologies

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Definitions

Graph representation concerns the layout of graph data in the linearly addressed storage of computers (main memory, SSD, hard disk, etc.). General objectives for graph representations are (1) to use little storage space (space-efficiency) while (2) allowing operations and queries on the graph to be executed in a short time (time-efficiency). Hence, every graph representation technique is subject to a specific space–time trade-off. It depends on the particular scenario (data, workload, available resources, etc.) whether the space–time trade-off of a certain representation technique is worthwhile.

Overview

Graph data comes with a large variation of graph data models, with RDF and the property graph model (PGM) being the most prominent ones. The specific graph representation depends on the data model. We introduce the most important concepts for graph representations as data–model-agnostic as possible. The discussion is structured in (1) primary representation of the graph...

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Author information

Authors and Affiliations

  1. SAP SE, Walldorf, Germany

    Marcus Paradies

  2. Technische Universität Dresden, Dresden, Germany

    Hannes Voigt

Authors
  1. Marcus Paradies
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  2. Hannes Voigt
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Corresponding authors

Correspondence to Marcus Paradies or Hannes Voigt .

Editor information

Editors and Affiliations

  1. School of Comp. Sci. and Engineering, University of New South Wales School of Comp. Sci. and Engineering, Eveleigh, New South Wales, Australia

    Sherif Sakr

  2. Sch of Info Techno, Building J12, University of Sydney Sch of Info Techno, Building J12, Sydney, Australia

    Albert Zomaya

Section Editor information

  1. Dresden Database Systems Group, Technische Universität Dresden, 01062, Dresden, Germany

    Hannes Voigt

  2. Department of Mathematics and Computer Science, Eindhoven University of Technology http://www.win.tue.nl/~gfletche/

    George Fletcher

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Paradies, M., Voigt, H. (2018). Graph Representations and Storage. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_211-1

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  • DOI: https://doi.org/10.1007/978-3-319-63962-8_211-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63962-8

  • Online ISBN: 978-3-319-63962-8

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