Intelligent Data Mining Techniques for Big Data in Cloud Computing: A Review

Authors

  • Baraa Mohammed Hasan Computer Department, College of Education for Pure Sciences, Wasit University, 52001 Al-Kut, Wasit, Iraq.
  • Fatima Shaker College of Computer Science and Information Technology, University of Al-Qadisiyah, Diwaniyah, Iraq.

DOI:

https://doi.org/10.29304/jqcsm.2025.17.42542

Keywords:

cloud computing, big data

Abstract

The rapid growth of data production across diverse fields created big data as an essential area for both academic study and practical application during recent times. The spread of cloud computing provides businesses with an adaptable infrastructure that enables economical and scalable processing and storage of enormous data collections. Popular use of modern data mining methods requires dealing with major technical obstacles while analyzing massive and complicated information collections. Data mining as an intelligent methodology emerged because it unites artificial intelligence methods with machine learning techniques together with statistical approaches and database operations to resolve these difficulties. In this review paper demonstrates extensive details about Intelligent data mining solutions that operate within cloud infrastructure while handling big data. The paper presents essential details about the basic principles followed by a classification scheme which includes techniques like classification and clustering alongside association rule mining with deep learning methods and their applications for big data needs. This study analyzes data mining system integration models for clouds while it assesses their operational abilities and scalability and makes note of essential difficulties including heterogeneity and real-time processing requirements.

Downloads

Download data is not yet available.

References

Saeed, N. & Husamaldin, Laden. (2021). Big Data Characteristics (V’s) in Industry. Iraqi Journal of Industrial Research. 8. 1-9. 10.53523/ijoirVol8I1ID52.

Buyya, R., Ramamohanarao, K., Leckie, C., Calheiros, R. N., Dastjerdi, A. V., & Versteeg, S. (2015). Big Data Analytics-Enhanced Cloud Computing: Challenges, Architectural Elements, and Future Directions. arXiv preprint arXiv:1510.06486.

Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2019). A Survey of Big Data Machine Learning Applications Optimization in Cloud Data Centers and Networks. arXiv preprint arXiv:1910.00731.

Dinov, I. D. (2023). Data Science and Predictive Analytics: Biomedical and Health Applications Using R (2nd ed.). Springer..

T. Ige and S. Adewale, "Implementation of Data Mining on a Secure Cloud Computing over a Web API using Supervised Machine Learning Algorithm," arXiv preprint arXiv:2208.06433, 2022. [Online]. Available:

S. Oesch, R. Gillen, and T. Karnowski, "An Integrated Platform for Collaborative Data Analytics," arXiv preprint arXiv:2012.09244, 2020. [Online]. Available:

Zhongzhi Shi. Big Data Mining in the Cloud. 7th International Conference on Intelligent Information Processing (IIP), Oct 2012, Guilin, China. pp.13-14, .

Alwan, H. B., & Ku-Mahamud, K. R. (2020). Big data: definition, characteristics, life cycle, applications, and challenges. IOP Conference Series: Materials Science and Engineering, 769(1), 012007.

Downloads

Published

2025-12-30

How to Cite

Mohammed Hasan, B., & Shaker, F. (2025). Intelligent Data Mining Techniques for Big Data in Cloud Computing: A Review. Journal of Al-Qadisiyah for Computer Science and Mathematics, 17(4), Comp. 104–120. https://doi.org/10.29304/jqcsm.2025.17.42542

Issue

Section

Computer Articles

Most read articles by the same author(s)