Temporal knowledge graph visualization: Capturing dynamic service interactions during cloud system failure cascade

This article introduces Temporal Knowledge Graphs (TKGs) as an innovative solution to the complex diagnostic challenges of modern cloud computing environments. Addressing the limitations of traditional static monitoring tools, TKGs capture the dynamic, time-dependent interactions between microservices that characterize transient failures in distributed systems. By modeling when and how services interact over time, TKGs enable enhanced root cause analysis through Graph Neural Networks that can detect temporal patterns invisible to conventional tools. The article demonstrates significant improvements in diagnostic capabilities, including reduced mean time to diagnosis, decreased false positive rates, and improved identification of causally-linked failure cascades. Through multiple case studies spanning cloud providers, healthcare IoT systems, and financial services, the article validates the effectiveness of TKG implementations across diverse operational contexts. The article provides a comprehensive analysis of TKG architecture, implementation considerations, performance metrics, and future research directions, establishing both theoretical foundations and practical guidance for next-generation cloud diagnostics systems.