PG-Keys: keys for property graphs - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2021

PG-Keys: keys for property graphs

Victor E Lee
  • Fonction : Auteur
Bei Li
  • Fonction : Auteur
Josh Perryman
  • Fonction : Auteur
Michael Schmidt
Juan Sequeda
  • Fonction : Auteur

Résumé

We report on a community effort between industry and academia to shape the future of property graph constraints. The standardization for a property graph query language is currently underway through the ISO Graph Query Language (GQL) project. Our position is that this project should pay close attention to schemas and constraints, and should focus next on key constraints. The main purposes of keys are enforcing data integrity and allowing the referencing and identifying of objects. Motivated by use cases from our industry partners, we argue that key constraints should be able to have different modes, which are combinations of basic restriction that require the key to be exclusive, mandatory, and singleton. Moreover, keys should be applicable to nodes, edges, and properties since these all can represent valid real-life entities. Our result is PG-Keys, a flexible and powerful framework for defining key constraints, which fulfills the above goals. PG-Keys is a design by the Linked Data Benchmark Council's Property Graph Schema Working Group, consisting of members from industry, academia, and ISO GQL standards group, intending to bring the best of all worlds to property graph practitioners. PG-Keys aims to guide the evolution of the standardization efforts towards making systems more useful, powerful, and expressive. CCS CONCEPTS • Information systems → Integrity checking; • Theory of computation → Data modeling; Database constraints theory.
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Dates et versions

hal-03189192 , version 1 (14-12-2021)

Identifiants

Citer

Renzo Angles, Angela Bonifati, Stefania Dumbrava, George Fletcher, Keith W Hare, et al.. PG-Keys: keys for property graphs. The 2021 International Conference on Management of Data (SIGMOD '21), Jun 2021, Xi'an, China. ⟨10.1145/3448016.3457561⟩. ⟨hal-03189192⟩
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