Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Graph Data Management Systems

  • Marcus ParadiesEmail author
  • Stefan PlantikowEmail author
  • Oskar van RestEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_82

Classification

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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References

  1. AgensGraph (2017) http://bitnine.net/
  2. Amazon Neptune (2017) https://aws.amazon.com/neptune/
  3. Angles R, Gutierrez C (2008) Survey of graph database models. ACM Comput Surv (CSUR) 40(1):1–39CrossRefGoogle Scholar
  4. Angles R, Arenas M, Barcelo P, Boncz P, Fletcher GHL, Gutierrez C, Lindaaker T, Paradies M, Plantikow S, Sequeda J, van Rest O, Voigt H (2017) G-CORE: a core for future graph query languages. https://arxiv.org/abs/1712.01550
  5. Brameller A, Allan RN, Hamam Y (1976) Sparsity: its practical application to systems analysis. Pitman, LondonzbMATHGoogle Scholar
  6. Cayley (2017) https://cayley.io/
  7. Erling O (2012) Virtuoso, a hybrid RDBMS/graph column store. IEEE Data Eng Bull 35(1):3–8Google Scholar
  8. Francis N, Green A, Guagliardo P, Libkin L, Lindaaker T, Marsault V, Plantikow S, Rydberg M, Selmer P, Taylor A (2017, Submitted) Cypher: an evolving query language for property graphsGoogle Scholar
  9. openCypher implementers group T (2017a) Cypher for Apache Spark. https://github.com/opencypher/cypher-for-apache-spark
  10. openCypher implementers group T (2017b) Cypher query language reference, version 9. https://github. com/opencypher/openCypher/blob/master/docs/open Cypher9.pdf
  11. Haubenschild M, Then M, Hong S, Chafi H (2016) ASGraph: a mutable multi-versioned graph container with high analytical performance. In: Proceedings of the fourth international workshop on graph data management experiences and systems, GRADES’16, pp 8: 1–8:6Google Scholar
  12. Hauck M, Paradies M, Fröning H, Lehner W, Rauhe H (2015) Highspeed graph processing exploiting main-memory column stores. In: Proceedings of the Euro-Par 2015 international workshops, pp 503–514CrossRefGoogle Scholar
  13. Hong S, Chafi H, Sedlar E, Olukotun K (2012) Green-Marl: a DSL for easy and efficient graph analysis. In: Proceedings of the 17th international conference on architectural support for programming languages and operating systems, ASPLOS’12, pp 349–362Google Scholar
  14. Hong S, Rodia NC, Olukotun K (2013) On fast parallel detection of strongly connected components (SCC) in small-world graphs. In: International conference for high performance computing, networking, storage and analysis, SC’13, pp 92:1–92:11Google Scholar
  15. Hong S, Depner S, Manhardt T, Van Der Lugt J, Verstraaten M, Chafi H (2015) PGX.D: a fast distributed graph processing engine. In: Proceedings of the international conference for high performance computing, networking, storage and analysis, SC’15, pp 58: 1–58:12Google Scholar
  16. Macko P, Marathe VJ, Margo DW, Seltzer MI (2015) LLAMA: efficient graph analytics using large multiversioned arrays. In: Proceedings of the 31st IEEE international conference on data engineering, ICDE’15, pp 363–374Google Scholar
  17. Malewicz G, Austern MH, Bik AJ, Dehnert JC, Horn I, Leiser N, Czajkowski G (2010) Pregel: a system for large-scale graph processing. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 135–146Google Scholar
  18. MemGraph (2017) https://memgraph.com/
  19. Microsoft (2017) Graph processing with SQL server and azure SQL database. https://docs.microsoft.com/en-us/sql/relational-databases/graphs/sql-graph-overview
  20. Neo4j (2017) https://neo4j.com
  21. Neumann T, Weikum G (2008) RDF-3X: a RISC-style engine for RDF. Proc VLDB Endow 1(1): 647–659CrossRefGoogle Scholar
  22. Ontotext (2017) https://ontotext.com/
  23. Oracle (2017b) PGQL 1.1 specification. http://pgql-lang.org/spec/1.1/
  24. Paradies M, Lehner W, Bornhövd C (2015) GRAPHITE: an extensible graph traversal framework for relational database management systems. In: Proceedings of the international conference on scientific and statistical database management, SSDBM’15, pp 29:1–29:12Google Scholar
  25. Paradies M, Kinder C, Bross J, Fischer T, Kasperovics R, Gildhoff H (2017) GraphScript: implementing complex graph algorithms in SAP HANA. In: Proceedings of the 16th international symposium on database programming languages, DBPL’17, pp 13:1–13:4Google Scholar
  26. Raman R, van Rest O, Hong S, Wu Z, Chafi H, Banerjee J (2014) PGX.ISO: parallel and efficient in-memory engine for subgraph isomorphism. In: Second international workshop on graph data management experiences and systems, GRADES’14, pp 1–6Google Scholar
  27. RDF4J E (2017) https://rdf4j.org/
  28. van Rest O, Hong S, Kim J, Meng X, Chafi H (2016) PGQL: a property graph query language. In: Proceedings of the fourth international workshop on graph data management experiences and systems, GRADES’16, p 7Google Scholar
  29. Rodriguez MA (2015) The gremlin graph traversal machine and language. In: ACM database programming languages conference, DBPL’15Google Scholar
  30. Roth NP, Trigonakis V, Hong S, Chafi H, Potter A, Motik B, Horrocks I (2017) PGX.D/Async: a scalable distributed graph pattern matching engine. In: Proceedings of the fifth international workshop on graph data-management experiences & systems, GRADES’17, pp 7:1–7:6Google Scholar
  31. Rudolf M, Paradies M, Bornhövd C, Lehner W (2013) The graph story of the SAP HANA database. In: Datenbanksysteme für Business, Technologie und Web (BTW), BTW’13, vol 214, pp 403–420Google Scholar
  32. Saad Y (2003) Iterative methods for sparse linear systems, 2nd edn. Society for Industrial and Applied Mathematics, PhiladelphiazbMATHCrossRefGoogle Scholar
  33. Sevenich M, Hong S, van Rest O, Wu Z, Banerjee J, Chafi H (2016) Using domain-specific languages for analytic graph databases. Proc VLDB Endow 9(13):1257–1268CrossRefGoogle Scholar
  34. Simmen D, Schnaitter K, Davis J, He Y, Lohariwala S, Mysore A, Shenoi V, Tan M, Xiao Y (2014) Large-scale graph analytics in aster 6: bringing context to big data discovery. Proc VLDB Endow 7(13):1405–1416CrossRefGoogle Scholar
  35. Stutz P, Bernstein A, Cohen W (2010) Signal/collect: graph algorithms for the (semantic) web. In: Proceedings of the 9th international semantic web conference on the semantic web – volume part I, ISWC’10. Springer, Berlin/Heidelberg, pp 764–780. http://dl.acm.org/citation.cfm?id=1940281.1940330Google Scholar
  36. Sun W, Fokoue A, Srinivas K, Kementsietsidis A, Hu G, Xie GT (2015) SQLGraph: an efficient relational-based property graph store. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 1887–1901Google Scholar
  37. Tanase I, Xia Y, Nai L, Liu Y, Tan W, Crawford J, Lin CY (2014) A highly efficient runtime and graph library for large scale graph analytics. In: Proceedings of workshop on graph data management experiences and systems, GRADES’14, pp 1–6Google Scholar
  38. TigerGraph (2017) https://www.tigergraph.com/
  39. Valstar LD, Fletcher GHL, Yoshida Y (2017) Landmark indexing for evaluation of label-constrained reachability queries. In: Proceedings of the 2017 ACM international conference on management of data, pp 345–358Google Scholar
  40. W3C (2013) SPARQL 1.1 query language. https://www.w3.org/TR/2013/REC-sparql11-query-20130321/

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.SAP SEWalldorfGermany
  2. 2.SAP SEBerlinGermany
  3. 3.Neo4jBerlinGermany
  4. 4.OracleCAUSA