4 research outputs found
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
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
The future is big graphs! A community view on graph processing systems
Graphs are, by nature, ‘unifying abstractions’ that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed