AN ANALYTICAL REVIEW OF MODERN ADAPTIVE METHODS FOR STREAM DATA PROCESSING IN REAL-TIME SYSTEMS

Real-time systems process continuous, non-stationary data streams while adhering to strict latency constraints and employ adaptive learning mechanisms to ensure a stable level of prediction reliability. Current methods for achieving this goal typically prioritize either model accuracy or computational scalability over the integration of an adaptive learning method into a streaming environment. The objective of this study is to systematically review state-of-the-art adaptive learning methods for real-time streaming data processing using a multi-step methodology including bibliometric analysis, a systematic literature review, and a structured comparative synthesis. A total of 58 studies were analyzed to identify patterns in adaptive capabilities, architectural integration approaches, and overall system performance evaluation. The results revealed a consistent tradeoff between adaptivity and deterministic latency, as well as a lack of cross-layer coordination and performance measurement. Based on the analytical synthesis of the reviewed literature, a conceptual cross-layer analytical framework supporting the integration of adaptive learning and distributed streaming systems is proposed. Methodological recommendations for the design of adaptive systems demonstrating a high level of performance while maintaining stable operation in dynamic, non-stationary environments are presented.

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