Data Compression for Active Diagnosis

开放嵌入式系统在线诊断的目标是定位和恢复开放系统中的故障，其中组件可以动态地加入和离开系统，以提供与安全相关的服务。在前人的工作中，介绍了一种基于语义网技术的在线诊断方法。使用SPARQL查询从感觉数据、状态信息和已经推断的诊断信息中导出新的诊断信息。尽管许多应用中的通信带宽非常有限，但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。该项目的主要目标是提高效率，减少开放式嵌入式系统中使用数据压缩进行在线诊断的开销。需要新的压缩技术和现有技术的调整来支持在线诊断的特定特征，例如大量部分相关的数据流，这些数据流需要作为动态和连续的时间窗口来存储和处理，以评估诊断查询。将压缩组件整合到在线诊断系统中还需要扩展关于诊断信息的推理过程和推理的时间安排。为解决这些问题而开发的算法将在不同的应用场景中进行原型实现和评估。

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.