Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Graph Query Languages

  • Renzo AnglesEmail author
  • Juan ReutterEmail author
  • Hannes VoigtEmail author
Living reference work entry
First Online:
DOI: https://doi.org/10.1007/978-3-319-63962-8_75-1
How to cite

Definition

A query language is a high-level computer language for the retrieval and modification of data held in databases or files. Query languages usually consist of a collection of operators which can be applied to any valid instances of the data structure types of a data model, in any combination desired.

In the context of graph data management, a graph query language (GQL) defines the way to retrieve or extract data which have been modeled as a graph and whose structure is defined by a graph data model. Therefore, a GQL is designed to support specific graph operations, such as graph pattern matching and shortest path finding.

Overview

Research on graph query languages has at least 30 years of history. Several GQLs were proposed during the 1980s, most of them oriented to study and define the theoretical foundations of the area. During the 1990s, the research on GQLs was overshadowed by the appearance of XML, which was arguably seen as the main alternative to relational databases...

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

References

  1. Abiteboul S, Hull R, Vianu V (1995) Foundations of databases. Addison-Wesley, ReadingGoogle Scholar
  2. Abiteboul S, Buneman P, Suciu D (1999) Data on the Web: from relations to semistructured data and XML. Morgan Kauffman, San FranciscoGoogle Scholar
  3. Angles R, Arenas M, Barceló P, Boncz PA, Fletcher GHL, Gutierrez C, Lindaaker T, Paradies M, Plantikow S, Sequeda J, van Rest O, Voigt H (2017a) G-CORE: a core for future graph query languages. The computing research repository abs/1712.01550Google Scholar
  4. Angles R, Arenas M, Barceló P, Hogan A, Reutter JL, Vrgoc D (2017b) Foundations of modern query languages for graph databases. ACM Comput Surv 68(5):1–40CrossRefGoogle Scholar
  5. Barceló P (2013) Querying graph databases. In: Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, PODS 2013, pp 175–188Google Scholar
  6. Barceló P, Libkin L, Lin AW, Wood PT (2012a) Expressive languages for path queries over graph-structured data. ACM Trans Database Syst (TODS) 37(4):31CrossRefGoogle Scholar
  7. Barceló P, Pérez J, Reutter JL (2012b) Relative expressiveness of nested regular expressions. In: Proceedings of the Alberto Mendelzon workshop on foundations of data management (AMW), pp 180–195Google Scholar
  8. Bienvenu M, Calvanese D, Ortiz M, Simkus M (2014) Nested regular path queries in description logics. In: Proceeding of the international conference on principles of knowledge representation and reasoning (KR)Google Scholar
  9. Bienvenu M, Ortiz M, Simkus M (2015) Navigational queries based on frontier-guarded datalog: preliminary results. In: Proceeding of the Alberto Mendelzon workshop on foundations of data management (AMW), p 162Google Scholar
  10. Bourhis P, Krötzsch M, Rudolph S (2014) How to best nest regular path queries. In: Informal Proceedings of the 27th International Workshop on Description LogicsGoogle Scholar
  11. Bourhis P, Krötzsch M, Rudolph S (2015) Reasonable highly expressive query languages. In: Proceeding of the international joint conference on artificial intelligence (IJCAI), pp 2826–2832Google Scholar
  12. Brijder R, Gillis JJM, Van den Bussche J (2013) The DNA query language DNAQL. In: Proceeding of the international conference on database theory (ICDT). ACM, pp 1–9Google Scholar
  13. Calvanese D, De Giacomo G, Lenzerini M, Vardi MY (2000) Containment of conjunctive regular path queries with inverse. In: Proceeding of the international conference on principles of knowledge representation and reasoning (KR), pp 176–185Google Scholar
  14. Calvanese D, De Giacomo G, Lenzerini M, Vardi MY (2002) Rewriting of regular expressions and regular path queries. J Comput Syst Sci (JCSS) 64(3):443–465MathSciNetCrossRefGoogle Scholar
  15. Consens M, Mendelzon A (1990) Graphlog: a visual formalism for real life recursion. In: Proceeding of the ACM symposium on principles of database systems (PODS), pp 404–416Google Scholar
  16. Cruz I, Mendelzon A, Wood P (1987a) A graphical query language supporting recursion. In: ACM special interest group on management of data 1987 annual conference (SIGMOD), pp 323–330CrossRefGoogle Scholar
  17. Cruz IF, Mendelzon AO, Wood PT (1987b) A graphical query language supporting recursion. In: Proceeding of the ACM international conference on management of data (SIGMOD), pp 323–330CrossRefGoogle Scholar
  18. Cruz IF, Mendelzon AO, Wood PT (1989) G+: recursive queries without recursion. In: Proceeding of the international conference on expert database systems (EDS). Addison-Wesley, pp 645–666Google Scholar
  19. Date CJ (1984) Some principles of good language design (with especial reference to the design of database languages). SIGMOD Rec 14(3):1–7CrossRefGoogle Scholar
  20. Dries A, Nijssen S, De Raedt L (2009) A query language for analyzing networks. In: Proceeding of the ACM international conference on information and knowledge management (CIKM). ACM, pp 485–494Google Scholar
  21. Fionda V, Pirrò G, Consens MP (2015) Extended property paths: writing more SPARQL queries in a succinct way. In: Proceeding of the conference on artificial intelligence (AAAI)Google Scholar
  22. Florescu D, Levy AY, Suciu D (1998) Query containment for conjunctive queries with regular expressions. In: Proceeding of the ACM symposium on principles of database systems (PODS), pp 139–148Google Scholar
  23. Haase P, Broekstra J, Eberhart A, Volz R (2004) A comparison of RDF query languages. In: Proceeding of the international Semantic Web conference (ISWC), pp 502–517CrossRefGoogle Scholar
  24. Harris S, Seaborne A (2013) SPARQL 1.1 query language. W3C recommendation. http://www.w3.org/TR/sparql11-query/
  25. Hellings J (2014) Conjunctive context-free path queries. In: Proceeding of the international conference on database theory (ICDT), pp 119–130Google Scholar
  26. Kostylev EV, Reutter JL, Romero M, Vrgoč D (2015) SPARQL with property paths. In: Proceeding of the international Semantic Web conference (ISWC), pp 3–18CrossRefGoogle Scholar
  27. Libkin L, Vrgoč D (2012) Regular path queries on graphs with data. In: Proceeding of the international conference on database theory (ICDT), pp 74–85Google Scholar
  28. Martín MS, Gutierrez C, Wood PT (2011) SNQL: a social networks query and transformation language. In: Proceeding of the Alberto Mendelzon workshop on foundations of data management (AMW)Google Scholar
  29. Miller JA, Ramaswamy L, Kochut KJ, Fard A (2015) Research directions for big data graph analytics. In: Proceeding of the IEEE international congress on big data, pp 785–794Google Scholar
  30. Prud’hommeaux E, Seaborne A (2008) SPARQL query language for RDF. W3C recommendation. http://www.w3.org/TR/rdf-sparql-query/
  31. Reutter JL, Romero M, Vardi MY (2015) Regular queries on graph databases. In: Proceeding of the international conference on database theory (ICDT), pp 177–194Google Scholar
  32. Rodriguez MA (2015) The Gremlin graph traversal machine and language. In: Proceeding of the international workshop on database programming languages. ACMGoogle Scholar
  33. Rudolf M, Voigt H, Bornhövd C, Lehner W (2014) SynopSys: foundations for multidimensional graph analytics. In: Castellanos M, Dayal U, Pedersen TB, Tatbul N (eds) BIRTE’14, business intelligence for the real-time enterprise, 1 Sept 2014. Springer, Hangzhou, pp 159–166Google Scholar
  34. Rudolph S, Krötzsch M (2013) Flag & check: data access with monadically defined queries. In: Proceeding of the symposium on principles of database systems (PODS). ACM, pp 151–162Google Scholar
  35. Santini S (2012) Regular languages with variables on graphs. Inf Comput 211:1–28MathSciNetCrossRefGoogle Scholar
  36. van Rest O, Hong S, Kim J, Meng X, Chafi H (2016) PGQL: a property graph query language. In: Proceeding of the workshop on graph data-management experiences and systems (GRADES)Google Scholar
  37. Voigt H (2017) Declarative multidimensional graph queries. In: Proceeding of the 6th European business intelligence summer schoole (BISS). LNBIP, vol 280. Springer, pp 1–37Google Scholar
  38. Wood PT (1990) Factoring augmented regular chain programs. In: Proceeding of the international conference on very large data bases (VLDB), pp 255–263Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Universidad de TalcaTalcaChile
  2. 2.Pontificia Universidad Catolica de ChileSantiagoChile
  3. 3.Dresden Database Systems GroupTechnische Universität DresdenDresdenGermany

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