References

Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M. and Bouchachia, A., 2014. A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4), pp.1-37.

Imbrea, A.I., 2021. Automated machine learning techniques for data streams. arXiv preprint arXiv:2106.07317.

Kanumula, S.S., 2024. AutoML and Enterprise AI: Challenges, Opportunities, and Innovations. Available at SSRN 5119389.

Mahmood, T. and Fatima, T., 2021. Concept drift in streaming data: A systematic literature review. KIET Journal of Computing and Information Sciences, 4(1), pp.17-17.

Meng Han, Zhiqiang Chen, Muhang Li, Hongxin Wu, Xilong Zhang, 2022. A survey of active and passive concept drift handling methods. https://doi.org/10.1111/coin.12520

Nie, H., Zhao, X., Yao, X., Jiang, Q., Bi, X., Ma, Y. and Sun, Y., 2023. Temporal-structural importance weighted graph convolutional network for temporal knowledge graph completion. Future Generation Computer Systems, 143, pp.30-39.

Perugu, P.K., 2024. Enterprise AI and AutoML: Overcoming Challenges for Smarter Automation. Available at SSRN 5147790.

Rameshwar Mhaske, 2025. Concept Drift Detection Gaps Degrading Performance in Machine Learning Systems. Available at: https://www.mhtechin.com/support/concept-drift-detection-gaps-degrading-performance-in-machine-learning-systems/ [Accessed on: 14th April 2026]

Shyaa, M.A., Zainol, Z., Abdullah, R., Anbar, M., Alzubaidi, L. and Santamaría, J., 2023. Enhanced intrusion detection with data stream classification and concept drift guided by the incremental learning genetic programming combiner. Sensors, 23(7), p.3736.

Soumik, M.S., Sarkar, M. and Rahman, M.M., 2021. Fraud Detection and Personalized Recommendations on Synthetic E-Commerce Data with ML. Research Journal in Business and Economics, 1(1a), pp.15-29.

Wang, X., Khan, A., Wang, J., Gangopadhyay, A., Busart, C. and Freeman, J., 2022. An edge–cloud integrated framework for flexible and dynamic stream analytics. Future Generation Computer Systems, 137, pp.323-335.

Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J. and Ghodsi, A., 2016. Apache spark: a unified engine for big data processing. Communications of the ACM, 59(11), pp.56-65.

Cite this Article as: G.Archana, N.Ananthkumar & C.Arunthathi  (2026), “adaptive analytics pipelines for concept drift in high- frequency big data environments”, International Journal of Advances in Engineering & Scientific Research,  Volume 13, Issue 2, 2026, pp 01-11.  DOI : https://doi.org/ 10.5281/zenodo.19665119  

References

Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M. and Bouchachia, A., 2014. A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4), pp.1-37.

Imbrea, A.I., 2021. Automated machine learning techniques for data streams. arXiv preprint arXiv:2106.07317.

Kanumula, S.S., 2024. AutoML and Enterprise AI: Challenges, Opportunities, and Innovations. Available at SSRN 5119389.

Mahmood, T. and Fatima, T., 2021. Concept drift in streaming data: A systematic literature review. KIET Journal of Computing and Information Sciences, 4(1), pp.17-17.

Meng Han, Zhiqiang Chen, Muhang Li, Hongxin Wu, Xilong Zhang, 2022. A survey of active and passive concept drift handling methods. https://doi.org/10.1111/coin.12520

Nie, H., Zhao, X., Yao, X., Jiang, Q., Bi, X., Ma, Y. and Sun, Y., 2023. Temporal-structural importance weighted graph convolutional network for temporal knowledge graph completion. Future Generation Computer Systems, 143, pp.30-39.

Perugu, P.K., 2024. Enterprise AI and AutoML: Overcoming Challenges for Smarter Automation. Available at SSRN 5147790.

Rameshwar Mhaske, 2025. Concept Drift Detection Gaps Degrading Performance in Machine Learning Systems. Available at: https://www.mhtechin.com/support/concept-drift-detection-gaps-degrading-performance-in-machine-learning-systems/ [Accessed on: 14th April 2026]

Shyaa, M.A., Zainol, Z., Abdullah, R., Anbar, M., Alzubaidi, L. and Santamaría, J., 2023. Enhanced intrusion detection with data stream classification and concept drift guided by the incremental learning genetic programming combiner. Sensors, 23(7), p.3736.

Soumik, M.S., Sarkar, M. and Rahman, M.M., 2021. Fraud Detection and Personalized Recommendations on Synthetic E-Commerce Data with ML. Research Journal in Business and Economics, 1(1a), pp.15-29.

Wang, X., Khan, A., Wang, J., Gangopadhyay, A., Busart, C. and Freeman, J., 2022. An edge–cloud integrated framework for flexible and dynamic stream analytics. Future Generation Computer Systems, 137, pp.323-335.

Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J. and Ghodsi, A., 2016. Apache spark: a unified engine for big data processing. Communications of the ACM, 59(11), pp.56-65.