Containment of Simple Conjunctive Regular Path Queries

Figueira, Diego; Godbole, Adwait; Krishna, Shankara Narayanan; Martens, Wim; Niewerth, Matthias; Trautner, Tina

doi:10.24963/kr.2020/38

“…GPML's graph patterns extend the celebrated language of Conjunctive Regular Path Queries (CRPQ) introduced in [17] and studied in a plethora of follow-up work (e.g. [9,10,[14][15][16]22]). CRPQs consist of variables (typically binding only to individual vertices and edges), path patterns specified by regular expressions over the edge labels, and comma-separated lists thereof.…”

Section: Keep Any Shortestmentioning

confidence: 99%

Graph Pattern Matching in GQL and SQL/PGQ

Deutsch¹,

Francis

²

,

Green

³

et al. 2022

Proceedings of the 2022 International Conference on Management of Data

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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.

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“…Calvanese et al [13] extended CRPQs to include an inverse operator, and showed that query containment is EXPSPACE-complete for this class of queries. More recently, Figuera et al [23] show that, by considering various sub-classes of CRPQs which are known to occur frequently in practice, the complexity of deciding containment can be reduced.…”

Section: Uery Containmentmentioning

confidence: 99%

Optimisation Techniques for Flexible SPARQL Queries

Frosini

¹

,

Poulovassilis

²

,

Wood

³

et al. 2022

ACM Trans. Web

1

0

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RDF datasets can be queried using the SPARQL language but are often irregularly structured and incomplete, which may make precise query formulation hard for users. The SPARQL AR language extends SPARQL 1.1 with two operators — APPROX and RELAX — so as to allow flexible querying over property paths. These operators encapsulate different dimensions of query flexibility, namely approximation and generalisation, and they allow users to query complex, heterogeneous knowledge graphs without needing to know precisely how the data is structured. Earlier work has described the syntax, semantics and complexity of SPARQL AR , has demonstrated its practical feasibility, but has also highlighted the need for improving the speed of query evaluation. In the present paper, we focus on the design of two optimisation techniques targeted at speeding up the execution of SPARQL AR queries and on their empirical evaluation on three knowledge graphs: LUBM, DBpedia and YAGO. We show that applying these optimisations can result in substantial improvements in the execution times of longer-running queries (sometimes by one or more orders of magnitude) without incurring significant performance penalties for fast queries.

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“…introduced in [17] and studied in a plethora of follow-up work (e.g. [9,10,[14][15][16]22]). CRPQs consist of variables (typically binding only to individual vertices and edges), path patterns specified by regular expressions over the edge labels, and comma-separated lists thereof.…”

Section: Keep Any Shortestmentioning

confidence: 99%

Graph Pattern Matching in GQL and SQL/PGQ

Deutsch¹,

Francis²,

Green³

et al. 2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

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.

show abstract

Containment of Simple Conjunctive Regular Path Queries

Cited by 8 publications

References 23 publications

Graph Pattern Matching in GQL and SQL/PGQ

Graph Pattern Matching in GQL and SQL/PGQ

Optimisation Techniques for Flexible SPARQL Queries

Graph Pattern Matching in GQL and SQL/PGQ

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