传感器数据流实时语义注释方法研究

为了对微环境监测平台上的传感器所捕获的异构、大量、连续的数据流进行语义注释，从而及时地根据语义上下文推理出新的或隐含的知识，以实现微环境监测平台的实时监测，对SASML映射语言和SDRM算法进行了研究和改进，设计了S-SASML映射语言和SDS2R算法，用于将传感器原始数据流转换为符合SOSA/SSN本体的RDF数据流；并利用线程池技术实现方法的高并发处理，提高了方法的实时性能。改进后的映射语言和算法实现了微环境监测平台对连续、大量的数据流的实时语义注释，不仅解决了动态传感器数据流语义注释的问题，而且避免了高频数据流导致的系统过载现象，具有稳定高效的处理能力，基本满足了微环境监测平台的需求，具有一定的应用价值。

Research on real-time semantic annotation method for sensor data stream

In order to conduct the semantic annotation to the heterogeneous, vast and continuous data flow which is captured from micro-environment monitoring platform, inference fresh or implicit knowledge timely according to a new semantic context, and realize real-time monitoring of the micro-environment monitoring platform, the SASML mapping language and the SDRM algorithm are researched and developed, and the S-SASML mapping language and SDS2R algorithm are designed to translate the original sensor data streams into the format of the RDF data streams of the SOSA/SSN. The thread pool techno-logy is used to implement high concurrent processing and improve the real-time performance of our proposed method. The improved mapping language and algorithm can realize the real-time semantic annotation for the continuous, vast data streams on the micro-environmental monitoring platform. The mapping language and algorithm can not only solve the dynamic pickup data flow semantic annotation problem, but also avoid the overload phenomenon caused by high frequency data streams, so that the proposed method has a stable and efficient processing capacity. It basicly meets the demand of micro-environment monitoring platform, and has some application value.