标题： SigSPARQL：在查询知识图谱时将信号作为一等公民 显示英文标题

标题： SigSPARQL: Signals as a First-Class Citizen When Querying Knowledge Graphs

摘要： 目的：网络物理系统（CPSs）整合计算和物理过程，从数千个传感器产生时间序列数据。知识图谱可以对这些数据进行上下文化，但目前适用于监控CPS的方法依赖于基于观察的方法。这限制了在传感器数据上表达计算的能力，尤其是在无法对采样同步性或采样率做出假设的情况下。方法论：我们提出了一种将知识图谱与信号集成的方法，该方法将运行时传感器数据建模为从时间到数据的函数。为了展示这种方法，我们引入了SigSPARQL，一种可以结合RDF数据和信号的查询语言。我们通过一个原型评估其技术可行性，并展示了其在典型CPS监控用例中的应用。发现：我们的方法使查询能够结合基于图的知识和信号，克服了基于观察方法的一些关键限制。开发的原型成功证明了可行性和适用性。价值：这项工作提出了一个基于查询的CPS监控方法，该方法整合了知识图谱和信号，缓解了基于观察方法的问题。通过利用系统知识，它使操作员能够在同一领域内的不同系统实例上运行单一查询。未来的工作将扩展SigSPARQL的信号函数并在大规模CPS部署中对其进行评估。 显示英文摘要

摘要： Purpose: Cyber-Physical Systems (CPSs) integrate computation and physical processes, producing time series data from thousands of sensors. Knowledge graphs can contextualize these data, yet current approaches that are applicably to monitoring CPS rely on observation-based approaches. This limits the ability to express computations on sensor data, especially when no assumptions can be made about sampling synchronicity or sampling rates. Methodology: We propose an approach for integrating knowledge graphs with signals that model run-time sensor data as functions from time to data. To demonstrate this approach, we introduce SigSPARQL, a query language that can combine RDF data and signals. We assess its technical feasibility with a prototype and demonstrate its use in a typical CPS monitoring use case. Findings: Our approach enables queries to combine graph-based knowledge with signals, overcoming some key limits of observation-based methods. The developed prototype successfully demonstrated feasibility and applicability. Value: This work presents a query-based approach for CPS monitoring that integrates knowledge graphs and signals, alleviating problems of observation-based approaches. By leveraging system knowledge, it enables operators to run a single query across different system instances within the same domain. Future work will extend SigSPARQL with additional signal functions and evaluate it in large-scale CPS deployments.