Data Compression for Active Diagnosis

开放式嵌入式系统在线诊断的目标是在开放式系统中定位和恢复故障，其中组件可以动态地加入和离开系统，以提供安全相关的服务。在以前的工作中，介绍了一种基于语义Web技术的在线诊断方法。使用SPARQL查询从传感器数据、状态信息和已经推断的诊断信息导出新的诊断信息。大量的诊断信息需要存储在本地实时数据库中，并在系统的组件之间进行通信，尽管许多应用中的通信带宽非常有限。该项目的主要目标是提高效率和减少开销的开放式嵌入式系统中使用数据压缩的在线诊断。需要新的压缩技术和现有技术的适应，以支持在线诊断的具体特点，如大量的部分相关的数据流，这需要被存储和处理的动态和连续的时间窗口的诊断查询的评估。将压缩组件集成到在线诊断系统中还需要在诊断信息的推理过程和推理的调度中进行扩展。解决这些问题的算法将在不同的应用场景中原型实现和评估。

The goal of online diagnosis for open embedded systems is the localization and recovery from failures in open systems, where components can dynamically join and leave the system in order to provide safety-relevant services. In previous work, an approach for online diagnosis based on semantic web technology was introduced. New diagnostic information is derived using SPARQL queries from sensory data, status information and already inferred diagnostic information. Large amounts of diagnostic information need to be stored in local real-time databases and communicated between components of the system, although communication bandwidths in many applications are very limited. The primary objective of the project is increased efficiency and the reduction of overhead for online diagnosis in open embedded systems using data compression. New compression techniques and the adaptation of existing techniques is required to support the specific characteristics of online diagnosis such as large numbers of partly correlated data streams, which need to be stored and processed as dynamic and continuous time windows for the evaluation of diagnostic queries. The integration of compression components into an online diagnosis system requires also extensions in the inference process on diagnostic information and the scheduling of the inference. The developed algorithms for solving these problems will be prototypically implemented and evaluated in different application scenarios.