$AWB^+$AWB+-$Tree$Tree: A Novel Width-Based Index Structure Supporting Hybrid Matching for Large-Scale Content-Based Pub/Sub Systems

Zhengyu Liao, Shiyou Qian, Zhonglong Zheng, Jian Cao, Guangtao Xue, Minglu Li

Abstract: Event matching is a key component in a large-scale content-based publish/subscribe system. The performance of most existing algorithms is easily affected by the subscription matching probability. In this article, we propose a new data structure, named $AWB^+$-$Tree$, which is based on the width of the predicates, to efficiently index the subscriptions. The most notable feature of $AWB^+$-$Tree$ is its ability to combine the advantages of different matching methods, thus achieving high and robust performance in dynamic environments. First, we implement both a forward matching method (AFM) and a backward matching method (ABM) based on $AWB^+$-$Tree$. Then, we introduce a hybrid matching method (AHM) that combines AFM and ABM. Moreover, we extend $AWB^+$-$Tree$ in three aspects: approximate matching, string type matching, and fine-grained parallelization. We conducted extensive experiments to evaluate the performance of the proposed matching algorithms on synthetic and real-world datasets. The experiment results reveal that AHM achieves a reduction in matching time by up to 53.8% compared to the state-of-the-art method. Additionally, AHM exhibits improved performance robustness, with up to a 76.9% reduction in terms of the standard deviation of matching time. Particularly in dynamic scenarios, AHM is at least 2.3 times faster and 41.3% more stable than its counterparts. Furthermore, by implementing parallelization, the matching speed of 8 threads can be accelerated by 4.16 times compared to the single-thread matching speed.