Auxo: A Temporal Graph Management System - Wentao Han; Kaiwei Li; Shimin Chen; Wenguang Chen (Big Data Mining and Analytics)
As real-world graphs are often evolving over time, interest in analyzing the temporal behavior of graphs has grown. Herein, we propose Auxo, a novel temporal graph management system to support temporal graph analysis. It supports both efficient global and local queries with low space overhead. Auxo organizes temporal graph data in spatio-temporal chunks. A chunk spans a particular time interval and covers a set of vertices in a graph. We propose chunk layout and chunk splitting designs to achieve the desired efficiency and the abovementioned goals. First, by carefully choosing the time split policy, Auxo achieves linear complexity in both space usage and query time. Second, graph splitting further improves the worst-case query time, and reduces the performance variance introduced by splitting operations. Third, Auxo optimizes the data layout inside chunks, thereby significantly improving the performance of traverse-based graph queries. Experimental evaluation showed that Auxo achieved 2.9× to 12.1× improvement for global queries, and 1.7× to 2.7× improvement for local queries, as compared with state-of-the-art open-source solutions.