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Foundations of Modern Query Languages for Graph Databases

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arxiv 1610.06264 v3 pith:DE7CIHJP submitted 2016-10-20 cs.DB

Renzo Angles , Marcelo Arenas , Pablo Barcelo , Aidan Hogan , Juan Reutter , Domagoj Vrgoc This is my paper · ORCID
classification cs.DB
keywords graphquerydiscussfeatureslanguagespatternsexpressionsgraphs
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We survey foundational features underlying modern graph query languages. We first discuss two popular graph data models: edge-labelled graphs, where nodes are connected by directed, labelled edges; and property graphs, where nodes and edges can further have attributes. Next we discuss the two most fundamental graph querying functionalities: graph patterns and navigational expressions. We start with graph patterns, in which a graph-structured query is matched against the data. Thereafter we discuss navigational expressions, in which patterns can be matched recursively against the graph to navigate paths of arbitrary length; we give an overview of what kinds of expressions have been proposed, and how they can be combined with graph patterns. We also discuss several semantics under which queries using the previous features can be evaluated, what effects the selection of features and semantics has on complexity, and offer examples of such features in three modern languages that are used to query graphs: SPARQL, Cypher and Gremlin. We conclude by discussing the importance of formalisation for graph query languages; a summary of what is known about SPARQL, Cypher and Gremlin in terms of expressivity and complexity; and an outline of possible future directions for the area.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Raqlet: Cross-Paradigm Compilation for Recursive Queries

    cs.DB 2025-08 reject novelty 6.0 of 10

    A cross-paradigm source-to-source compiler for recursive queries, using PGIR, DLIR, and SQIR intermediate representations, with static analyses and optimizations, but only preliminary evaluation and no formal proofs.

  2. 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.

  3. Towards an Integrated Graph Algebra for Graph Pattern Matching with Gremlin (Extended Version)

    cs.DB 2019-08 conditional novelty 4.0 of 10

    Gremlin match()-step pattern matching traversals are mapped to an integrated graph algebra built from consolidated operators, providing a formal basis for query compilation.

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