Abstract
The technology relates to an autonomous cloud platform service that combines sub-millisecond in-flight stream interception, memory-bounded mathematical sketching, deep time-series neural forecasting, generative foundation model reasoning, and closed-loop reinforcement learning. The architecture provides proactive, dual-mode quality validation that isolates corrupted data prior to storage, forecasts pipeline health degradation hours before threshold breaches occur, and automatically generates verified code patches to repair upstream pipeline failures. Keywords: Data quality assurance, predictive anomaly detection, stream interception, dead-letter quarantine, mathematical sketching, generative artificial intelligence, pipeline remediation, distributed cloud computing.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
N/A and , "Autonomous Data Quality and Anomaly Remediation for Distributed Cloud Systems", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12888