Mnemonic: A Parallel Subgraph Matching System for Streaming Graphs
Bibek Bhattarai, Howie Huang

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…
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Taxonomy
TopicsGraph Theory and Algorithms · Advanced Database Systems and Queries · Software System Performance and Reliability
