In-Database Graph Analytics With Recursive Sparql

Works on knowledge graphs and graph-based data management often focus either on graph query languages or on frameworks for graph analytics, where there has been little work in trying to combine both approaches. However, many real-world tasks conceptually involve combinations of these approaches: a graph query can be used to select the appropriate data, which is then enriched with analytics, and then possibly filtered or combined again with other data by means of a query language. In this paper we propose a language that is well-suited for both graph querying and analytical tasks. We propose a minimalistic extension of SPARQL to allow for expressing analytical tasks over existing SPARQL infrastructure; in particular, we propose to extend SPARQL with recursive features, and provide a formal syntax and semantics for our language. We show that this language can express key analytical tasks on graphs (in fact, it is Turing complete). Moreover, queries in this language can also be compiled into sequences of iterations of SPARQL update statements. We show how procedures in our language can be implemented over off-the-shelf SPARQL engines, with a specialised client that can leverage database operations to improve the performance of queries. Results for our implementation show that procedures for popular analytics currently run in seconds or minutes for selective sub-graphs (our target use-case).