Optimising Queries for Pattern Detection Over Large Scale Temporally Evolving Graphs

Large-scale graph processing and Stream processing are two distinct computational paradigms for big data processing. Graph processing deals with computation on graphs of billions of vertices and edges. However, large-scale graph processing frameworks mostly work on graphs that do not change over time, while on the other end of the spectrum, stream processing operates on a continuous stream of data in real-time. Modern-day graphs change very rapidly over time, and finding patterns in temporally evolving graphs could reveal a lot of insights that can not be unveiled using traditional graph computations. We have proposed a novel framework called FlowGraph which could find patterns in dynamic and temporally evolving graphs. Computations on large-scale graphs are iterative and take multiple steps before final results can be calculated, which is very different from stream processing which is one-shot computation. Therefore, the most critical bottleneck of such a system is the time required to process the query. In this work, we have proposed a query optimization technique that could reduce the time required to process the pattern. The proposed system has an optimization technique that could reduce the time required to process the pattern, especially those related to the temporal evolution of the graph. Our method shows for eight clauses the execution time is reduced by 75%, we also proved that this improvement is not affected by the scaling of the graph or the change of elements in given clauses.