论文标题
助记符：流式图的并行子图匹配系统
Mnemonic: A Parallel Subgraph Matching System for Streaming Graphs
论文作者
论文摘要
在大型高度连接数据集中找到模式对于业务发展和科学研究中的价值发现至关重要。这项工作着重于流图上的子图匹配问题，该问题在从社交网络分析到网络安全等各种现实世界应用程序中提供了实用性。每个应用程序都会构成不同的控制参数，包括匹配，数据流类型和搜索粒度的限制。现有子图匹配系统的问题驱动的设计使它们在申请不同的问题域中具有挑战性。本文介绍了助记符，这是一种可编程系统，可提供高级API，并使各种子图匹配解决方案的发展民主化。重要的是，助记符还提供关键的数据管理功能和优化，以支持长期运行的高速跨关系图流的实时处理。该实验证明了助记符的多功能性，因为它以多达两个数量级以优于几个最先进的系统。
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.
