Management and Analysis of Big Graph Data: Current Systems and Open Challenges View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Martin Junghanns , André Petermann , Martin Neumann , Erhard Rahm

ABSTRACT

Many big data applications in business and science require the management and analysis of huge amounts of graph data. Suitable systems to manage and to analyze such graph data should meet a number of challenging requirements including support for an expressive graph data model with heterogeneous vertices and edges, powerful query and graph mining capabilities, ease of use as well as high performance and scalability. In this chapter, we survey current system approaches for management and analysis of “big graph data”. We discuss graph database systems, distributed graph processing systems such as Google Pregel and its variations, and graph dataflow approaches based on Apache Spark and Flink. We further outline a recent research framework called Gradoop that is build on the so-called Extended Property Graph Data Model with dedicated support for analyzing not only single graphs but also collections of graphs. Finally, we discuss current and future research challenges. More... »

PAGES

457-505

References to SciGraph publications

  • 2014-12. The Stratosphere platform for big data analytics in THE VLDB JOURNAL
  • 2015-02. RDF in the clouds: a survey in THE VLDB JOURNAL
  • Book

    TITLE

    Handbook of Big Data Technologies

    ISBN

    978-3-319-49339-8
    978-3-319-49340-4

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-49340-4_14

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