Derivation of parallel-efficient structural recursive functions from declarative graph queries

Graphs play an important role today in managing big data, while structural recursion is powerful to process graphs by its flexibility. Designing efficient structural recursive functions to query distributed graphs is still a big challenge because of the performance constraints imposed by the bulk semantics of structural recursion. In this paper, we propose a solution that systematically generates parallel-efficient structural recursive functions from high-level declarative graph queries. Therefore, the complexity in developing efficient structural recursive functions is relaxed by our solution.