Streaming Insights Uncovering Patterns with Adaptive Learning and Data Mining

Abstract

In the era of big data and continuous information flow, the utilization of adaptive learning and data mining techniques is paramount for extracting meaningful insights from streaming datasets. This paper explores the fundamental methodology of sampling, focusing on random sampling and the efficient alternative, reservoir sampling, in the context of data streams with indeterminate durations. Additionally, the study delves into the technique of sketching, offering a compact and efficient means of summarizing and processing rapidly arriving data. Addressing the challenges posed by concept drift in data stream analysis, the paper introduces Adaptive Multi-Strategy Learning, a dynamic approach that combines diverse learning strategies to enhance model performance across evolving contexts. The proposed hybrid ensemble learning approach, combining diverse learning algorithms, emerges as a versatile and powerful tool for uncovering patterns in streaming data, offering valuable insights for real-time trend analysis, heavy-hitter detection, and cardinality estimation.