Extending RTLola with External Data Queries

2026-08-06

AI Summary

This work addresses the challenge of efficiently integrating external data sources into existing stream-based monitoring systems. To this end, it extends RTLola with a unified external data query capability that supports both static databases and dynamic APIs, and introduces a specialized backend architecture tailored for spatiotemporal data. By incorporating k-d trees for geospatial indexing, static type checking, and an asynchronous response mechanism, the system achieves substantial performance gains while preserving safety guarantees. Empirical evaluation in an aircraft compliance verification scenario demonstrates that the proposed approach outperforms mainstream database systems, confirming its effectiveness and efficiency.

Abstract

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.

Problem

• stream-based monitoring
• external data sources
• data integration
• temporal properties
• runtime monitoring

Innovation

• stream-based monitoring
• external data queries
• RTLola
• geospatial backend
• k-d trees