Real-Time Analytics and Processing Techniques for Streaming Big Data in Healthcare and Smart Environments

The increasing prevalence of streaming big data from various sources, particularly Internet of Things (IoT) devices, presents significant challenges for real-time analytics. Handling the sheer volume, velocity, and diversity of this data requires sophisticated data engineering solutions. Traditional data processing methods are often inadequate for processing large volumes of heterogeneous streaming data effectively. This paper outlines key considerations and explores techniques for managing and processing streaming big data in real-time, focusing on applications in healthcare monitoring and smart environments. We discuss the importance of low-latency processing, data preprocessing, and the imperative for robust security and privacy measures. The study highlights the utilization of stream processing frameworks like Apache Kafka and Apache Spark for real-time data ingestion and analysis. Furthermore, it investigates the integration of machine learning algorithms, such as Decision Trees and Streaming Linear Regression, for tasks like health status prediction, and Auto encoders and DQN for smart city threat detection. Experimental evaluations demonstrate the feasibility and performance of these approaches on various datasets, including historical medical data and simulated streaming data. The results show that these techniques can achieve high accuracy in real-time prediction and offer potential benefits in resource utilization.