Abstract: In pervasive computing environments, wide deployment of sensor devices has generated an unprecedented volume of atomic events. However, most applications such as healthcare, surveillance and facility management, as well as environmental monitoring require such events to be filtered and correlated for complex pattern detection. Therefore how to extract interesting, useful and complex events from low-level atomic events is becoming more and more important in daily life. At present, there are a lot of researches of complex event detection, and each has its own particular research points. Some pay attention to the time information, especially the importance of time interval; some research into the complex event detection in distributed data sources; recently some propose the probabilistic data management on complex event detection. Due to the importance of complex event detection, this paper analyzes the challenges in the research of complex event detection, and gives a survey of existing researches from three aspects including event types, time information, and precision of data. Finally, some open issues and future researches are given.

Key words: pervasive computing, sensor, complex event detection, time interval, probabilistic data

摘要： 普适计算环境中, 传感器设备的大规模使用产生了数量巨大、错综复杂的原子事件, 而现实中的许多应用却更注重复合事件的检测, 例如：健康护理、监督设施管理、环境/安全监控等, 因此如何从这些底层的原子事件中抽取人们感兴趣的、有用的复合事件就变得越来越重要。目前, 针对复合事件检测有大量的研究, 其内容各有侧重。有的重视时间因素, 特别强调时间段的重要性; 有的研究分布式数据源中的复合事件检测; 近期有人提出了不确定性数据上的复合事件检测。由于复合事件检测日益重要, 对复合事件检测研究中存在的挑战性问题进行了分析, 从事件类型、时间因素和数据的精确程度3个方面归纳总结了复合事件检测现有的研究成果, 并指出了未来的发展方向。

关键词: 普适计算, 传感器, 复合事件检测, 时间段, 不确定性数据