In this paper, we study the problem of evaluating persistent queries over streaming graphs in a principled fashion. These queries need to be evaluated over unbounded and very high speed graph streams. We define a streaming graph model and a streaming graph query model incorporating navigational queries, subgraph queries and paths as first-class citizens. To support this full-fledged query model we develop a streaming graph algebra that describes the precise semantics of persistent graph queries with their complex constructs. We present transformation rules and describe query formulation and plan generation for persistent graph queries over streaming graphs. Our implementation of a streaming graph query processor based on the dataflow computational model shows the feasibility of our approach and allows us to gauge the high performance gains obtained for query processing over streaming graphs.

We study persistent query evaluation over streaming graphs, which is becoming increasingly important. We focus on navigational queries that determine if there exists a path between two entities that satis es a user-speci ed constraint. We adopt the Regular Path Query (RPQ) model that speci es navigational patterns with labeled constraints. We propose deterministic algorithms to e ciently evaluate persistent RPQs under both arbitrary and simple path semantics in a uniform manner. Experimental analysis on real and synthetic streaming graphs shows that the proposed algorithms can process up to tens of thousands of edges per second and e ciently answer RPQs that are commonly used in realworld workloads.