Type-safe Monitoring of Parameterized Streams

2026-03-11

AI Summary

This work addresses memory safety challenges in stream monitoring arising from unbounded data domains—such as an unbounded number of airspace participants—which can compromise the integrity of dynamic data structures. The authors integrate parameterized streams into the RTLola framework, enabling unified management of unbounded instance sets while employing a default-value mechanism to ensure safe memory accesses. They propose a static analysis method based on a refinement type system that, despite the undecidability of runtime errors, guarantees every memory reference either succeeds or safely falls back to a default value. Empirical evaluation across multiple benchmarks, including autonomous aerial vehicle monitoring, demonstrates the approach’s effectiveness and performance, significantly enhancing the runtime safety of monitoring components.

Technology Category

• Data Mining & Knowledge Management: Data Stream Mining
• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Stream-based monitoring is a real-time safety assurance mechanism for complex cyber-physical systems such as unmanned aerial vehicles. The monitor aggregates streams of input data from sensors and other sources to give real-time statistics and assessments of the system's health. Since the monitor is a safety-critical component, it is mandatory to ensure the absence of runtime errors in the monitor. Providing such guarantees is particularly challenging when the monitor must handle unbounded data domains, like an unlimited number of airspace participants, requiring the use of dynamic data structures. This paper provides a type-safe integration of parameterized streams into the stream-based monitoring framework RTLola. Parameterized streams generalize individual streams to sets of an unbounded number of stream instances and provide a systematic mechanism for memory management. We show that the absence of runtime errors is, in general, undecidable but can be effectively ensured with a refinement type system that guarantees all memory references are either successful or backed by a default value. We report on the performance of the type analysis on example specifications from a range of benchmarks, including specifications from the monitoring of autonomous aircraft.

Problem

• type safety
• parameterized streams
• runtime errors
• cyber-physical systems
• stream-based monitoring

Innovation

• type-safe monitoring
• parameterized streams
• refinement types
• RTLola
• runtime error prevention