Efficient Partitioning and Query Processing of Spatio-Temporal Graphs with Trillion Edges

Real-world graphs often contain spatio-temporal information and evolve over time. Compared with static graphs, spatio-temporal graphs present more significant challenges in data volume, data velocity, and query processing. In this paper, we define a formal spatio-temporal graph model based on real-world applications, and propose PAST, a framework for efficient PArtitioning and query processing of Spatio-Temporal graphs. Our experimental results show that PAST improves query performance by orders of magnitude compared to state-of-the-art solutions.