A Ph.D. Awarded to Researcher Mohammed Mohammed Hasan from the Department of Information Systems at the College of Computer
Researcher Mohammed Mohammed Hasan Zayed earned his Ph.D. with distinction in Computing and Information Science, specializing in Data Science in the Department of Information Systems, at the College of Computer and Information Technology, Sana’a University. His dissertation, entitled “A Novel Intelligent Model Based on Optimal Jumps for Creating Data Sampling from Big Data,” was successfully defended on Saturday, 25 Sha’ban 1446 AH (corresponding to February 24, 2025).
The examination and evaluation committee consisted of:
•Professor Dr. Fadhl Mutahar Ba’alawi – Principal Supervisor and Committee Member
•Professor Dr. Basheer Mohammed Al-Maqalrah – External Examiner and Chair of the Committee
•Associate Professor Mukhtar Mohammed Ghaylan – Internal Examiner and Committee Member
The aims of the dissertation were to:
•Develop an intelligent model for creating representative data samples from big data using an optimal jumps methodology.
•Improve sampling methods to achieve a balance between accuracy and computational performance.
•Reduce the size of input data while preserving critical information to support analysis and prediction tasks.
•Achieve tangible improvements in processing speed compared to traditional methods of big data analysis.
•Enable AI and machine learning applications to work more efficiently when handling large volumes of data.
The dissertation yielded important findings, notably:
•Demonstrating the effectiveness of the optimal jumps model in reducing data volume while maintaining representation accuracy.
•Achieving a higher accuracy rate than traditional random sampling methods.
•Enhancing computational resource efficiency, thus reducing both the cost and the time required for big data analysis.
•Confirming that the proposed model can be applied in various fields such as healthcare, e-commerce, financial analysis, and scientific research.
•Outperforming existing sampling models in terms of speed and accuracy in sample extraction.
Based on these findings, the researcher recommends:
•Implementing the proposed model within big data analytics systems in institutions and companies to improve decision-making efficiency.
•Expanding research to test the model on diverse types of big data across multiple fields.
•Refining the model by integrating advanced AI techniques, such as deep neural networks and adaptive algorithms.
•Incorporating the model with big data processing platforms like Apache Hadoop and Spark to enhance performance and scalability.
•Employing the model in critical areas such as cybersecurity and healthcare to assess its impact on improving big data analytics.
A number of academics, researchers, students, and interested attendees, as well as the researcher’s colleagues and family members, were present at the defense.
