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Finding patterns in large highly connected datasets is critical for valuediscovery in business development and scientific research. This work focuses onthe problem of subgraph matching on streaming graphs, which provides utility ina myriad of real-world applications ranging from social network analysis tocybersecurity. Each application poses a different set of control parameters,including the restrictions for a match, type of data stream, and searchgranularity. The problem-driven design of existing subgraph matching systemsmakes them challenging to apply for different problem domains. This paperpresents Mnemonic, a programmable system that provides a high-level API anddemocratizes the development of a wide variety of subgraph matching solutions.Importantly, Mnemonic also delivers key data management capabilities andoptimizations to support real-time processing on long-running, high-velocitymulti-relational graph streams. The experiments demonstrate the versatility ofMnemonic, as it outperforms several state-of-the-art systems by up to twoorders of magnitude.

Mnemonic: A Parallel Subgraph Matching System for Streaming Graphs