扩展RTLola以支持外部数据查询
Extending RTLola with External Data Queries
AI总结：
该研究扩展了RTLola以支持外部数据查询，解决了现有流监测器处理大型外部数据源的局限，其自定义地理空间后端性能优于先进数据库系统。
AI中文摘要：
基于流的监测能够简洁地指定复杂的时间属性，但现有基于流的监测器在处理大型外部数据源时存在局限，这类任务更适合由专门的数据管理系统处理。我们通过为基于流的监测器添加查询外部数据源的能力来解决这些局限，在RTLola中实现了该方法，并研究了延迟响应处理、返回数据的类型检查和运行时错误管理等挑战。统一接口可将现有系统无缝集成到该方法中，如静态数据库或动态端点（例如天气API）。我们使用航空领域的规范进行评估，结果显示，基于k-d树的自定义地理空间后端的性能优于最先进的数据库系统。
英文摘要：
Stream-based monitoring enables the concise specification of complex temporal properties. However, existing stream-based monitors are limited when dealing with large external data sources, a task that is better handled by specialized data management systems. We address these limitations by extending stream-based monitors with the ability to query external data sources. We implement this approach in RTLola and investigate challenges such as handling delayed responses, type checking of returned data, and runtime error management. A unified interface enables the seamless integration of existing systems into our approach, such as static databases or dynamic endpoints, e.g. a weather API. Our evaluation using specifications from the aviation domain also shows that a custom geospatial backend based on k-d trees outperforms state-of-the-art database systems.
