👉 Predictive Distributed Adaptive Sampling (PDAS) is a novel, distributed machine learning technique designed to efficiently handle large-scale, sequential data by dynamically adjusting the sampling strategy based on real-time predictions. It operates by dividing data into overlapping, adaptive partitions and selecting samples from these partitions using a probabilistic model that predicts the most informative data points for training. This approach balances computational efficiency and model accuracy by prioritizing samples that maximize information gain, reducing redundant computations while maintaining robustness to data variability. PDAS excels in scenarios like video processing, time-series analysis, and sequential data modeling, where traditional batch methods struggle with scalability or latency. Its distributed nature allows parallel processing across nodes, making it suitable for big data environments with heterogeneous or streaming inputs.
