Scaling-Out Streaming Graph Processing

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Graphs are natural representation for complex, inter-structured data arising in many domains and applications ranging from bioinformatics to real-time fraud-detection. Many of these real-world graphs are massive in size and continuously evolve overtime - edges are streaming at a very high rate. These streaming graphs pose unique data management challenges as they cannot be feasibly stored locally, and it is difficult to answer queries in real-time. The existing research and commercial systems are not able to handle these graphs and workloads. The primary objective of this research is to investigate robust system architectures and design a Streaming Graph Management System (SGMS) that can process a large number of persistent queries on large streaming graphs. In particular, we intend to study load balancing and distributed query processing techniques for scale-out architectures as well as single-pass, non-blocking operators for persistent query execution.