Real-Time Monitoring of LTL Properties in Distributed Stream Processing Applications

Stream processing frameworks have become key enablers of real-time data processing in modern distributed systems. However, robust and scalable mechanisms for verifying temporal properties are often lacking in existing systems. To address this gap, a new runtime verification framework is proposed that integrates linear temporal logic (LTL) monitoring into stream processing applications, such as Apache Spark. The approach introduces reusable LTL monitoring patterns designed for seamless integration into existing streaming workflows. Our case study, applied to real-time financial data monitoring, demonstrates that LTL-based monitoring can effectively detect violations of safety and liveness properties while maintaining stable latency. A performance evaluation reveals that although the approach introduces computational overhead, it scales effectively with increasing data volume. The proposed framework extends beyond financial data processing and is applicable to domains such as real-time equipment failure detection, financial fraud monitoring, and industrial IoT analytics. These findings demonstrate the feasibility of real-time LTL monitoring in large-scale stream processing environments while highlighting trade-offs between verification accuracy, scalability, and system overhead.