SIM: A fast real-time graph stream summarization with improved memory efficiency and accuracy

Abstract

A graph stream composed of sequentially approaching arriving edges is commonly utilized to represent complicated structured data in interactive application systems. Since graph streams are extraordinarily vast and high velocity, efficient storage and analysis of graph streams face serious challenges. Current graph stream summarization schemes have effectively achieved the storage and management of graph streams. Unfortunately, they either cannot accomplish real-time queries or have low overall query accuracy. To this end, a novel summary structure, named Shared Interaction Matrix (SIM), is proposed for real-time queries rapidly and accurately with smaller memory. SIM is designed as a two-layer adjacency matrix with different structures to improve memory efficiency while preserving the key of heavy edges to support real-time measurement. Moreover, SIM leverages shared hash technology and an integral replacement strategy to boost insertion query speed and query accuracy. The performance of SIM is evaluated by conducting extensive experiments on the CPU and OVS platform. The experimental results show that SIM significantly enhances measurement accuracy and reduces insertion and query processing latency by 39.21%–93.50% while achieving real-time queries, compared with the state-of-the-art schemes.