面向实时事件流的复杂事件处理方法

复杂事件处理是一种动态环境下对事件流进行分析的技术。复杂事件处理技术通常基于有限状态自动机实现，匹配过程中会在事件流上产生大量且重叠的部分匹配，有限状态自动机需维护大量的重复匹配状态，导致基于该技术的方法都会出现冗余计算的问题。为了提高复杂事件处理的匹配效率，提出了使用复杂事件实例覆盖技术来实现复杂事件处理的方法。通过设计临时匹配链式分区存储结构以及基于此结构的匹配算法，来利用复杂事件实例覆盖减少冗余计算，从而实现匹配效率的提升。在模拟数据集和真实数据集上进行了实验测试与分析，与两种常用的复杂事件处理技术进行比较。实验表明，提出方法能够在保证匹配正确性的同时有效地减少匹配过程中的冗余计算，提高整体匹配效率。

Complex event processing method over real-time event streams

Complex event processing is a technology for analyzing the event streams in a dynamic environment. Complex event processing technology is usually implemented based on finite state automaton. During the matching process, a large number of overlapping partial matches will be generated by the event stream. The finite state automaton needs to maintain a large number of repeated matching states, which leads to the problem of redundant calculation in the methods based on this technology. In order to improve the matching efficiency of complex event processing, this paper proposed a method of using complex event instance coverage technology to realize complex event processing. By designing a temporary matching chain partition storage structure and matching algorithms based on this structure, redundant calculations could be reduced using complex event instance coverage, thereby achieving an improvement in matching efficiency. Experiments were performed on simulated and real datasets, and compared with two commonly used complex event processing technologies. The experimental results show that the proposed method can effectively reduce the redundant computation in the matching process while ensuring the correctness of the matching, and improve the overall matching efficiency.