Mnemonic: A Parallel Subgraph Matching System for Streaming Graphs

TL;DR

Mnemonic is a versatile, programmable system for real-time subgraph matching on streaming graphs, outperforming existing solutions significantly and adaptable to various application-specific constraints.

Contribution

It introduces Mnemonic, a high-level API system that simplifies development and optimizes real-time subgraph matching across diverse streaming graph applications.

Findings

Outperforms state-of-the-art systems by up to 100x

Supports real-time processing of high-velocity graph streams

Demonstrates versatility across multiple application domains

Abstract

Finding patterns in large highly connected datasets is critical for value discovery in business development and scientific research. This work focuses on the problem of subgraph matching on streaming graphs, which provides utility in a myriad of real-world applications ranging from social network analysis to cybersecurity. Each application poses a different set of control parameters, including the restrictions for a match, type of data stream, and search granularity. The problem-driven design of existing subgraph matching systems makes them challenging to apply for different problem domains. This paper presents Mnemonic, a programmable system that provides a high-level API and democratizes the development of a wide variety of subgraph matching solutions. Importantly, Mnemonic also delivers key data management capabilities and optimizations to support real-time processing on long-running, high-velocity multi-relational graph streams. The experiments demonstrate the versatility of Mnemonic, as it outperforms several state-of-the-art systems by up to two orders of magnitude.