Streaming machine learning algorithms with streaming big data systems

Abstract

As the era of big data unfolds, the need for real-time analytics and decision-making becomes increasingly crucial. Streaming big data systems, designed to process and analyse data in motion, have emerged as a pivotal solution for handling vast streams of continuously arriving information. This research delves into the synergy between streaming big data systems and machine learning algorithms, aiming to harness the power of real-time insights. We explore the challenges and opportunities presented by the dynamic nature of streaming data, emphasizing the importance of adapting traditional machine learning methodologies to suit the evolving requirements of streaming environments.The research begins with an overview of streaming big data systems, laying the foundation for understanding the unique characteristics of data in motion. We then delve into the selection and adaptation of machine learning algorithms that are well-suited for continuous learning and updating. Key aspects of the research include the preprocessing and feature extraction techniques tailored for real-time data streams, ensuring the effective utilization of streaming machine learning algorithms. The paper provides insights into the challenges of model training and updating in a dynamic environment, emphasizing the importance of accuracy and efficiency.