Xiangyang Gou‘s paper “LM-SRPQ: Efficiently Answering Regular Path Query in Streaming Graphs”, which focuses on regular path queries in streaming graphs, has been accepted by VLDB 2024.

Regular path query (RPQ) is a basic operation for graph data analysis, and persistent RPQ in streaming graphs is a new-emerging research topic. This kind of queries requires to continuously maintain vertex-pairs which are connected by paths satisfying a given regular expression while the streaming graph is updated. In this paper, we propose a novel algorithm for persistent RPQ in streaming graphs, named LM-SRPQ. It solves persistent RPQ with a combination of tree-based intermediate result materialization and real-time graph traversal. Compared to prior art, it merges redundant storage and computation, achieving higher memory and time efficiency. We carry out extensive experiments with both real-world and synthetic streaming graphs to evaluate its performance. Experiment results confirm its superiority compared to prior art in both memory and time efficiency.