Updating Graph Databases with Cypher

International audienc

G-CORE a core for future graph query languages

We report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider supporting a query language with two key characteristics. First, it should be composable, meaning, that graphs are the input and the output of queries. Second, the graph query language should treat paths as first-class citizens. Our result is G-CORE, a powerful graph query language design that fulfills these goals, and strikes a careful balance between path query expressivity and evaluation complexity

G-CORE a core for future graph query languages

We report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider supporting a query language with two key characteristics. First, it should be composable, meaning, that graphs are the input and the output of queries. Second, the graph query language should treat paths as first-class citizens. Our result is G-CORE, a powerful graph query language design that fulfills these goals, and strikes a careful balance between path query expressivity and evaluation complexity

Graph Pattern Matching in GQL and SQL/PGQ

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

An experimental study of context-free path query evaluation methods

\u3cp\u3eContext-free path queries extend regular path queries for increased expressiveness. A context-free grammar is used to recognize accepted paths by their label strings, or traces. Such queries arise naturally in graph analytics, e.g., in bioinformatics applications. Currently, the practical performance of methods for context-free path query evaluation is not well understood. In this work, we study three state of the art context-free path query evaluation methods. We measure the performance of these methods on diverse query workloads on various data sets and compare their results. We showcase how these evaluation methods scale as graphs get bigger and queries become larger or more ambiguous. We conclude that state of the art solutions are not able to cope with large graphs as found in practice.\u3c/p\u3

An experimental study of context-free path query evaluation methods

Context-free path queries extend regular path queries for increased expressiveness. A context-free grammar is used to recognize accepted paths by their label strings, or traces. Such queries arise naturally in graph analytics, e.g., in bioinformatics applications. Currently, the practical performance of methods for context-free path query evaluation is not well understood. In this work, we study three state of the art context-free path query evaluation methods. We measure the performance of these methods on diverse query workloads on various data sets and compare their results. We showcase how these evaluation methods scale as graphs get bigger and queries become larger or more ambiguous. We conclude that state of the art solutions are not able to cope with large graphs as found in practice

G-CORE a core for future graph query languages

We report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider supporting a query language with two key characteristics. First, it should be composable, meaning, that graphs are the input and the output of queries. Second, the graph query language should treat paths as first-class citizens. Our result is G-CORE, a powerful graph query language design that fulfills these goals, and strikes a careful balance between path query expressivity and evaluation complexity

G-CORE a core for future graph query languages

\u3cp\u3eWe report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider supporting a query language with two key characteristics. First, it should be composable, meaning, that graphs are the input and the output of queries. Second, the graph query language should treat paths as first-class citizens. Our result is G-CORE, a powerful graph query language design that fulfills these goals, and strikes a careful balance between path query expressivity and evaluation complexity.\u3c/p\u3

G-CORE a core for future graph query languages

We report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider supporting a query language with two key characteristics. First, it should be composable, meaning, that graphs are the input and the output of queries. Second, the graph query language should treat paths as first-class citizens. Our result is G-CORE, a powerful graph query language design that fulfills these goals, and strikes a careful balance between path query expressivity and evaluation complexity