Toward high efficiency for content-based multi-attribute event matching via hybrid methods

Abstract

Event matching is a core in decoupled end-to-end communications, which are extensively applied to various areas. Event matching seeks the subscriptions that match a given event from a subscription set, however, this work becomes increasingly complicated in content-based multi-attribute scenarios, where events and subscriptions are formed in content, and described by multiple attributes. In addition, large-scale systems are easier to suffer from severe degradation in event matching performance. To this end, this paper presents a high-efficiency content-based multi-attribute event matching algorithm, called HEM (hybrid event matching), which is hybridized by 2 different methods. In HEM, the matching on each single attribute (called single-attribute matching) is processed by a triangle-based matching method or a direct matching method dynamically. All single-attribute matchings are sorted via a fast near-optimal algorithm, and each of them is carried out sequentially. In this manner, the searching space of event matching shrinks gradually, so that the searching performance is boosted along with the process of event matching. Experiments are conducted to evaluate HEM comprehensively, where it is observed that HEM outperforms 3 state-of-the-art counterparts (TAMA, H-TREE and REIN) in main criteria, such as event matching time, insertion time and deletion time. Moreover, the gap of performance between HEM and the counterparts enlarges with the increase of system scale.

创新点

在数据分发领域中,事件匹配是其核心环节,它负责查找与某事件匹配的所有接收者。在基于内容的多属性发布/订阅系统中,由于属性维度的上升,内容规模的扩大,使得传统的事件匹配算法难以有效支撑大规模、高维度的系统级应用。基于此,本文提出了一种联合利用两种匹配方法的事件匹配机制,包含一种新颖的利用解析几何理论的方法和一种直接匹配方法。两种方法通过优化算法动态使用,使得搜索空间在事件匹配过程中不断收缩,从而有效提升了事件匹配的速度。同时,该机制的检索结构能够高效支持快速的订阅插入和删除操作,因此,在高动态发布/订阅系统中具有优异的性能。我们将本文所提出机制和多个相关领域的最新算法进行了详细的实验比较,结果表明,与这些参考算法相比,该机制具有最短的事件匹配时间,以及最短的订阅插入和删除时间。