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Graph Data Management Systems

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Encyclopedia of Big Data Technologies

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The abundance and diversity of massive-scale, graph-structured data and the ever-growing interest of large enterprise companies to analyze them are the key drivers of the recent advances in graph data management research. Further, graph data management has witnessed a steady increase in applications from various industry domains, such as social media, logistics and transportation, gas and oil utility networks, finance, public security, and the pharmaceutical industry.

The widespread adoption of graph technology and the diversity in requirements for the aforementioned application domains are the key drivers for the development of a variety of graph data management systems (gdmss). These gdmss are typically tailored to a specific graph workload, which is distinctive to the application domain and drives the design of the core system components, such as the primary/secondary storage, the query execution engine, and the user interface(s).

From a data model perspective,...

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Author information

Authors and Affiliations

  1. SAP SE, Walldorf, Germany

    Marcus Paradies

  2. SAP SE, Berlin, Germany

    Marcus Paradies

  3. Neo4j, Berlin, Germany

    Stefan Plantikow

  4. Oracle, CA, USA

    Oskar van Rest

Authors
  1. Marcus Paradies
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  2. Stefan Plantikow
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  3. Oskar van Rest
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Corresponding authors

Correspondence to Marcus Paradies , Stefan Plantikow or Oskar van Rest .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. School of Information Technologies, Sydney University, Sydney, Australia

    Albert Y. Zomaya

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Paradies, M., Plantikow, S., Rest, O.v. (2019). Graph Data Management Systems. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_82

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