Open Access
Data Pipeline Architecture for Real-Time Analytics
Maria Santos
1
,
Ahmed Hassan
2
,
Jan Kowalski
3
,
Ahmed Hassan
2
,
Jan Kowalski
3
1
University of São Paulo, Brazil
2
Cairo University, Egypt
3
Warsaw University of Technology, Poland
Abstract
This research explores advanced techniques for this research area. We introduce a hybrid model combining advanced algorithms with modern techniques to improve accuracy. Our experiments on diverse datasets demonstrate superior performance compared to existing baseline methods. The proposed framework achieves 27% accuracy on standard benchmark datasets.
Keywords
natural language processing,text mining,sentiment analysis,NLP,transformers
How to Cite
Santos, M., Hassan, A., & Kowalski, J. (2022). Data Pipeline Architecture for Real-Time Analytics. Nivo Light - International Journal of Research & Innovation, 4(1), 6–13. https://doi.org/10.28051/ojstest-112
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