Large-scale continuous subgraph queries on streams

TL;DR

This paper reviews methods for real-time, continuous subgraph pattern matching on streaming graphs, highlighting challenges, solutions, and architectural features relevant for high-performance graph analytics in streaming data environments.

Contribution

It synthesizes existing research on continuous query systems and adapts key concepts for scalable, real-time subgraph matching in streaming graphs.

Findings

Identifies important semantics and constraints for streaming graph analytics

Discusses architectural features for high-performance graph processing

Connects continuous query techniques to streaming graph pattern matching

Abstract

Graph pattern matching involves finding exact or approximate matches for a query subgraph in a larger graph. It has been studied extensively and has strong applications in domains such as computer vision, computational biology, social networks, security and finance. The problem of exact graph pattern matching is often described in terms of subgraph isomorphism which is NP-complete. The exponential growth in streaming data from online social networks, news and video streams and the continual need for situational awareness motivates a solution for finding patterns in streaming updates. This is also the prime driver for the real-time analytics market. Development of incremental algorithms for graph pattern matching on streaming inputs to a continually evolving graph is a nascent area of research. Some of the challenges associated with this problem are the same as found in continuous query (CQ) evaluation on streaming databases. This paper reviews some of the representative work from the exhaustively researched field of CQ systems and identifies important semantics, constraints and architectural features that are also appropriate for HPC systems performing real-time graph analytics. For each of these features we present a brief discussion of the challenge encountered in the database realm, the approach to the solution and state their relevance in a high-performance, streaming graph processing framework.