The application of machine learning to infinite, high-velocity data streams presents unique computational challenges, particularly regarding memory constraints, delayed label availability, and the phenomenon of concept drift. While robust algorithmic frameworks exist for online learning, deploying these models in commercial, highly distributed environments often results in severe latency and ingestion bottlenecks. This paper proposes a cloud-native architectural solution utilizing Amazon Web Services to construct a serverless pipeline optimized for data stream mining. By deploying asynchronous Python middleware integrated with distributed Hoeffding Trees and adaptive windowing drift detectors, the proposed system ingests high-frequency telemetry, isolating statistical distribution shifts in real-time. Preliminary architectural evaluations demonstrate that moving the inference and drift-detection logic to the network edge significantly reduces processing latency and accelerates ensemble model adaptation. This methodology provides computer scientists and data engineers with a deterministic, highly scalable technological foundation for evaluating non-stationary data streams in critical infrastructure such as smart grids and financial networks.
The application of Automated Machine Learning to infinite, high-velocity data streams represents a critical frontier in Big Data Science. Traditional hyperparameter optimization frameworks, such as grid search or Bayesian optimization, are inherently designed for static, batch-le…
Abstract The rapid growth of big data and the increasing complexity of deep learning applications have created significant challenges for traditional data processing infrastructures, particularly in terms of scalability, performance, and resource efficiency. This study presents a…
Abstract The rapid growth of data-intensive applications has necessitated the development of scalable and efficient architectures for cloud-based machine learning and data analysis. This study proposes a scalable, distributed, and fault-tolerant architecture designed to address t…
The measurement of climate risk and its influence on accounting-based equity valuation has become a critical mandate in empirical financial research. Traditional methodologies utilize log-linear valuation models and historical panel data to observe how investors adjust their rela…
The efficient allocation of freshwater resources in small catchments represents a critical challenge in environmental economics. While theoretical models propose supply-side water trading and group-level "water clubs" to mitigate resource depletion, the empirical testing of these…
The increasing frequency of severe climate anomalies and natural disasters has destabilized global insurance markets, precipitating a widespread retreat of private disaster insurance. Financial economists modeling the economics of natural hazard risks are frequently constrained b…