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

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