FlowGraph: Distributed temporal pattern detection over dynamically evolving graphs

Temporally evolving graphs are an indispensable requisite of modern-day big data processing pipelines. Existing graph processing systems mostly focus on static graphs and lack the essential support for pattern detection and event processing in graph-shaped data. On the other hand, stream processing systems support event and pattern detection, but they are inadequate for graph processing. This work lies at the intersection of the graph and stream processing domains with the following objectives: (i) It introduces the syntax of a language for the detection of temporal patterns in large-scale graphs. (ii) It presents a novel data structure called distributed label store (DLS) to efficiently store graph computation results and discover temporal patterns within them. The proposed system, called FlowGraph, unifies graph-shaped data with stream processing by observing graph changes as a stream flowing into the system. It provides an API to handle temporal patterns that predicate on the results of graph computations with traditional graph computations.