Abstract:

The rapid growth of dynamic web applications has caused the continuous production of high-velocity and several user interaction data, which is infeasible to address using traditional batch-processing systems that are only good at delivering insights. Currently, the solution for this problem is real-time analytics. However, it has many applied methodologies that are not always adaptable to the environment for continuous learning mechanisms. This paper introduces an adaptive real-time analytics framework that implements streaming data processing, machine learning, and feedback- driven model refinement into a unified architecture. The data in the framework incrementally enters the system and is processed using sliding window techniques, while predictive models generate the insights. The feedback loop is used to further fine-tune the model and adapt it to the changes in the data distribution. This results in scalability, the speed of an approach, and the model’s system being closer to the real-world performance capability, leading to conducive decision-making in dynamic web environments. The framework has been applied in different case domains, including e-commerce, social media analytics, and real-time monitoring systems. In conclusion, it can be argued that the study presented a sophisticated illustration of how the adaptive and computational real-time analytics framework could be devised.

Keywords:

Real-Time Analytics, Streaming Data Processing, Machine Learning, Adaptive Systems, Web Applications, Predictive Analytics, Feedback Learning.

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Methods