Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Graph Data Models

  • Claudio Gutierrez
  • Jan Hidders
  • Peter T. Wood
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_81-1



Following the classic definition of Codd, a data model comprises three basic components: the data structure(s), a transformation and query language, and integrity constraints. Under this conceptualization, a graph data model is characterized as follows:
  • The data (and possibly its schema) is represented by graphs or by generalizations of the concept of a graph (e.g., hypergraphs, hypernodes).

  • The manipulation of data is done by graph transformations or by operations capturing features such as paths, neighborhoods, graph patterns, etc.

  • The integrity constraints enforce the consistency of schemas and graph properties that are relevant to the particular model.

In this entry, we will focus on the data structures part, since query languages for graphs are treated in other entries in this work. Thus we will use the notion “graph data model” for the data structure of a graph data model.



This is a preview of subscription content, log in to check access.


  1. Angles R, Gutierrez C (2008) Survey of graph database models. ACM Comput Surv 40(1):1:1–1:39Google Scholar
  2. Angles R, Arenas M, Barceló P, Hogan A, Reutter JL, Vrgoč D (2017) Foundations of modern graph query languages. ACM Comput Surv 50(5):68: 1–68:40Google Scholar
  3. Barceló Baeza P (2013) Querying graph databases. In: Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGAI symposium on principles of database systems. ACM, New York, pp 175–188Google Scholar
  4. Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Sci Am 284(5):34–43Google Scholar
  5. Bourhis P, Reutter JL, Suárez F, Vrgoč D (2017) JSON: data model, query languages and schema specification. In: Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI symposium on principles of database systems. ACM, New York, pp 123–135Google Scholar
  6. Brickley D, Guha R (2014) RDF schema 1.1. http://www.w3.org/TR/2014/REC-rdf-schema-20140225/, W3C Recommendation 25 Feb 2014
  7. Chen PPS (1976) The entity-relationship model – toward a unified view of data. ACM Trans Database Syst 1(1): 9–36Google Scholar
  8. Cyganiak R, Wood D, Lanthaler M (2014) RDF 1.1 concepts and abstract syntax. http://www.w3.org/TR/2014/REC-rdf11-concepts-20140225/, W3C Recommendation 25 Feb 2014
  9. Gyssens M, Paradaens J, Den Bussche JV, Gucht D (1990) A graph-oriented object database. In: Proceedings of the 9th symposium on principles of database systems. ACM Press, pp 417–424Google Scholar
  10. Harris S, Seaborne A (2013) SPARQL 1.1 query language. http://www.w3.org/TR/2013/REC-sparql11-query-20130321/, W3C Recommendation 21 Mar 2013
  11. Lehmann FW, Rodin EY (eds) (1992) Semantic networks in artificial intelligence. International series in modern applied mathematics and computer science, vol 24. Pergamon Press, OxfordGoogle Scholar
  12. Levene M, Poulovassilis P (1991) An object-oriented data model formalised through hypergraphs. IEEE Trans Knowl Data Eng 6(3):205–224Google Scholar
  13. Papakonstantinou Y, Garcia-Molina H, Widom J (1995) Object exchange across heterogeneous information sources. In: Proceedings of the eleventh international conference on data engineering. IEEE, p 251–260Google Scholar
  14. Wood PT (2009) Graph database. In: Liu L, Özsu MT (eds) Encyclopedia of database systems. Springer, Boston, pp 1263–1266Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversidad de ChileSantiagoChile
  2. 2.Vrije Universiteit BrusselBrusselsBelgium
  3. 3.Birkbeck, University of LondonLondonUK

Section editors and affiliations

  • Hannes Voigt
    • 1
  • George Fletcher
    • 2
  1. 1.Dresden Database Systems GroupTechnische Universität DresdenDresdenGermany
  2. 2.Department of Mathematics and Computer ScienceEindhoven University of Technology