Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data
arXiv:2412.11800v4 Announce Type: replace Abstract: Extracting anomaly causality facilitates diagnostics once monitoring systems detect system faults. Identifying anomaly causes in large systems involves investigating a broader set of monitoring variables across multiple subsystems. However, learning graphical causal models (GCMs) comes with a significant computational burden that restrains the applicability of most existing methods in real-time and large-scale deployments. In addition, modern monitoring applications for large systems often generate large amounts of binary alarm flags, and the
The increasing complexity and scale of AI systems and critical infrastructure demand more efficient real-time anomaly detection and causality identification to maintain operational integrity.
This development allows for more scalable and computationally efficient AI-driven diagnostics in large systems, critical for maintaining stability and performance across various complex technological deployments.
The ability to discover anomaly causality in large systems, especially those generating binary alarm flags, becomes feasible for real-time and large-scale applications due to computational efficiency improvements.
- · AI-driven diagnostic platforms
- · Large-scale system operators
- · Cloud infrastructure providers
- · Cybersecurity firms
- · Legacy manual diagnostic processes
- · Inefficient causal inference methods
Improved system reliability and reduced downtime across various industries relying on large-scale interconnected systems.
Accelerated adoption of AI in critical infrastructure management, leading to more resilient and autonomous operations.
The development of highly complex and self-healing systems, potentially reducing the need for human intervention in anomaly resolution.
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