arXiv:2510.06063v2 Announce Type: replace-cross Abstract: Modern enterprises generate vast streams of time series metrics when monitoring complex systems, known as observability data. Unlike conventional time series from domains such as climate, observability data are zero-inflated, highly stochastic, and exhibit minimal temporal structure. Despite their importance, observability datasets remain underrepresented in public benchmarks due to proprietary restrictions and privacy concerns. Existing datasets are often anonymized and normalized, removing scale information and limiting their use for
The proliferation of complex digital systems has outpaced the understanding and interpretability of their monitoring data, necessitating advancements in AI-driven observability.
This new dataset addresses a critical gap in AI research by providing realistic, high-fidelity observability data, enabling the development of more robust AI models for system monitoring and anomaly detection.
The availability of a multi-modal, realistic observability dataset will accelerate research in AI for IT operations, leading to more effective and autonomous management of large-scale digital infrastructure.
- · AI/ML researchers
- · Cloud infrastructure providers
- · DevOps teams
- · Observability platforms
- · Companies with legacy monitoring systems
- · Manual IT operations processes
Improved AI models for anomaly detection and root cause analysis in complex systems.
Reduced operational costs and downtime for enterprises relying on sophisticated digital infrastructure.
Increased automation in IT operations, potentially leading to fewer human interventions and a shift in IT skill requirements.
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Read at arXiv cs.LG