Extending SPARQL with graph functions

Much of the early domain-specific success with graph analytics has been with algorithms whose results are based on global graph structure. An example of such an algorithm is betweenness centrality, whose value for any vertex potentially depends on the number of shortest paths between all pairs of vertices in the entire graph. YarcData's UrikaTM customers use SPARQL's graph-oriented pattern-matching capabilities, but many of them also require a capability to call graph functions such as betweenness centrality. This customer feedback led us to combine SPARQL 1.1's query capabilities with classical and emerging graph-analytic algorithms (e.g., community detection, shortest path, betweenness, BadRank). With this capability, a SPARQL query can select a specific subgraph of interest, pass that subgraph to a graph algorithms for deep analysis, and then pass those results back to an enclosing SPARQL query that post-processes those results as needed. With the Summer 2014 Urika release, we have extended the SPARQL implementation with a graph-function capability and a small set of built-in graph functions. We describe our design approach and our experiences with this first release, including anecdotal evidence of dramatically higher performance. Built-in graph functions represent an important step in the maturation of graph analysis and SPARQL. As common motifs emerge from use cases, those motifs may be mapped to specific graph functions that can be highly tuned for much higher performance than will be possible for SPARQL. Identifying those motifs and developing the underlying graph functions to accelerate their execution is a topic of intense effort industry-wide. Graph functions merged with SPARQL provide a new mechanism by which third-party graph-algorithm developers may expose their algorithms to widespread use.