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Graph Pattern Matching in GQL and SQL/PGQ

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arxiv 2112.06217 v1 pith:5XLIF4NX submitted 2021-12-12 cs.DB

Alin Deutsch , Nadime Francis , Alastair Green , Keith Hare , Bei Li , Leonid Libkin , Tobias Lindaaker , Victor Marsault
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Wim Martens Jan Michels Filip Murlak Stefan Plantikow Petra Selmer Hannes Voigt Oskar van Rest Domagoj Vrgoč Mingxi Wu Fred Zemke
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classification cs.DB
keywords graphcomplementsdatagpmlgroupinternationaljtc1languages
verification ladder T0 review T1 audit T2 compute T3 formal
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As graph databases become widespread, JTC1 -- the committee in joint charge of information technology standards for the International Organization for Standardization (ISO), and International Electrotechnical Commission (IEC) -- has approved a project to create GQL, a standard property graph query language. This complements a project to extend SQL with a new part, SQL/PGQ, which specifies how to define graph views over an SQL tabular schema, and to run read-only queries against them. Both projects have been assigned to the ISO/IEC JTC1 SC32 working group for Database Languages, WG3, which continues to maintain and enhance SQL as a whole. This common responsibility helps enforce a policy that the identical core of both PGQ and GQL is a graph pattern matching sub-language, here termed GPML. The WG3 design process is also analyzed by an academic working group, part of the Linked Data Benchmark Council (LDBC), whose task is to produce a formal semantics of these graph data languages, which complements their standard specifications. This paper, written by members of WG3 and LDBC, presents the key elements of the GPML of SQL/PGQ and GQL in advance of the publication of these new standards.

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  1. Graph Neural Networks on Graph Databases

    cs.LG 2024-11 conditional novelty 6.0 of 10

    Training a GNN by querying a graph database for neighbor samples and features reduces memory use enough to train on small machines, but is much slower than in-memory training.

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