Unlocking The Power Of Machine Learning In Big Data A Scoping Survey

Open Access Unlocking the power of machine learning in big data: a scoping survey Тип публикации: Journal Article Дата публикации: 2025-12-01 scimago Q1 SJR: 1.37 CiteScore: 11.9 Impact factor: — ISSN: 26667649 Краткое описание Machine learning (ML) plays a crucial role in big data (BD) by serving as the cornerstone of efficient data processing and analysis. In particular, ML provides BD with the ability to extract valuable insights from the large data sets.

Therefore, this study conducted a scoping survey to define the role of ML in BD by exploring its history and evolution. Subsequently, a framework of ML in BD is proposed, emphasizing its practical applications in addressing the challenges presented by the volume, velocity, variety, and veracity of data. Moreover, BD analytics is described, showcasing how ML paradigms contribute to decision-making. This is followed by an illustration of real-world applications across diverse industries regarding the transformative impact of ML on BD.

The survey findings highlight the integration of ML in BD considering volume, variety, velocity, and reliability. Scalable storage solutions, advanced computational architectures, and distributed ML are the avenues for shaping the ML landscape. Similarly, embedding intelligence in preprocessing and interoperable models is an avenue considering veracity. Real-time processing frameworks, temporally aware ML, and edge--computing integration are avenues considering velocity. Automated data quality assurance, explainable artificial intelligence and transparency, and blockchain technology for data provenance are avenues in veracity. Найдено Ничего не найдено, попробуйте изменить настройки фильтра.

Для доступа к списку цитирований публикации необходимо авторизоваться. Топ-30 Журналы Издатели - Мы не учитываем публикации, у которых нет DOI. - Статистика публикаций обновляется еженедельно. Вы ученый? Создайте профиль, чтобы получать персональные рекомендации коллег, конференций и новых статей. Метрики 18 Всего цитирований: 18 Цитирований c 2025: 16 (100%) Цитировать ГОСТ | RIS | BibTex | MLA Цитировать ГОСТ Скопировать Surur F. M. et al. Unlocking the power of machine learning in big data: a scoping survey // Data Science and Management. 2025. Vol. 8. No. 4. pp. 519-535.

ГОСТ со всеми авторами (до 50) Скопировать Surur F. M., Mamo A. A., Gebresilassie B. G., Mekonen K., Golda A., Behera R. K., Kumar K. Unlocking the power of machine learning in big data: a scoping survey // Data Science and Management. 2025. Vol. 8. No. 4. pp. 519-535.

Цитировать RIS Скопировать TY - JOUR DO - 10.1016/j.dsm.2025.02.004 UR - https://linkinghub.elsevier.com/retrieve/pii/S2666764925000104 TI - Unlocking the power of machine learning in big data: a scoping survey T2 - Data Science and Management AU - Surur, Fadil Mohammed AU - Mamo, Abiy Abinet AU - Gebresilassie, Bealu Girma AU - Mekonen, Kidus AU - Golda, Abenezer AU - Behera, Rajat Kumar AU - Kumar, Kumod PY - 2025 DA - 2025/12/01 PB - Elsevier SP - 519-535 IS - 4 VL - 8 SN - 2666-7649 ER - Цитировать BibTex (до 50 авторов) Скопировать @article{2025_Surur, author = {Fadil Mohammed Surur and Abiy Abinet Mamo and Bealu Girma Gebresilassie and Kidus Mekonen and Abenezer Golda and Rajat Kumar Behera and Kumod Kumar}, title = {Unlocking the power of machine learning in big data: a scoping survey}, journal = {Data Science and Management}, year = {2025}, volume = {8}, publisher = {Elsevier}, month = {dec}, url = {https://linkinghub.elsevier.com/retrieve/pii/S2666764925000104}, number = {4}, pages = {519--535}, doi = {10.1016/j.dsm.2025.02.004} } Цитировать MLA Скопировать Surur, Fadil Mohammed, et al.

“Unlocking the power of machine learning in big data: a scoping survey.” Data Science and Management, vol. 8, no. 4, Dec. 2025, pp. 519-535. https://linkinghub.elsevier.com/retrieve/pii/S2666764925000104. Ошибка в публикации?

People Also Asked

• Unlocking the power of machine learning in big data: a ...
• Unlocking the future: How machine learning tr | EurekAlert!