Temporal pattern recognition in large scale graphs

Many application domains involve monitoring the temporal evolution of large-scale graph data structures. Unfortunately, this task is not well supported by modern programming paradigms and frameworks for large-scale data processing. This paper presents ongoing work on the implementation of FlowGraph, a framework to recognize temporal patterns over properties of large-scale graphs. FlowGraph combines the programming paradigm of traditional graph computation frameworks with the temporal pattern detection capabilities of Complex Event Recognition (CER) systems. In a nutshell, FlowGraph distributes the graph data structure across multiple nodes that also contribute to the computation and store partial results for pattern detection. It exploits temporal properties to defer as much as possible expensive computations, to sustain a high rate of changes.