Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data
Causal discovery from binary alarm sequences in large-scale systems remains challenging due to the joint requirements of computational efficiency, sparse dependency modeling, and semantic capture of state transitions. Method: This paper proposes the first causal inference framework specifically designed for binary anomaly data. It introduces a sparse causal testing mechanism based on an improved Granger causality test, integrating flag-sequence feature encoding, adaptive graph-structure learning, and dynamic edge pruning. Contribution/Results: The framework explicitly models both the state-transition semantics and extreme sparsity inherent in binary data—novelty not addressed by prior work. By combining link compression with accuracy-aware pruning, it achieves scalable yet precise causal discovery. Evaluated on the CMS detector readout box system and IT monitoring datasets, it significantly reduces computational overhead while improving causal F1-score by a medium margin, thereby enabling effective real-time root-cause diagnosis.