Stream Smart: Scalable AI Frameworks for Real-Time Cloud Analytics

Akshita Chaudhary, Amit Sharma, Milind

Stream Smart: Scalable AI Frameworks for Real-Time Cloud Analytics

Authors

Akshita Chaudhary, Amit Sharma, Milind

Keywords:

Real-time analytics, scalable AI, cloud computing, data streaming, adaptive frameworks, StreamSmart, big data, predictive modeling.

Abstract

As the volume of data from IoT devices, enterprise systems, and user interactions continues toevolve exponentially, real-time analytics is a key enabler for intelligent decision making.Legacy batch processing and stagnant AI models cannot cope well with high velocity, volume,and diversity of contemporary data streams

References

Zaharia, M., et al. “Structured Streaming: A Declarative API for Real-Time Applications.” Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data, 2016.

Carbone, P., et al. “State Management in Apache Flink.” Proceedings of the VLDB Endowment, vol. 10, no. 12, pp. 1718–1729, 2017.

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Published

2024-08-15

How to Cite

Akshita Chaudhary, Amit Sharma, Milind. (2024). Stream Smart: Scalable AI Frameworks for Real-Time Cloud Analytics. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 5412–5424. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3211