USING REGRESSION MODEL TO ANALYZE THE WORKLOAD OF EXAMINATION DATABASE SERVER

Junaid Fazlani; Aftab Ahmed Chandio; Qamar-ul-Nisa Chandio

doi:10.71146/kjmr873

Authors

Junaid Fazlani Institute of Mathematics and Computer Science, University of Sindh, Jamshoro, Pakistan. Author
Aftab Ahmed Chandio Institute of Mathematics and Computer Science, University of Sindh, Jamshoro, Pakistan. Author
Qamar-ul-Nisa Chandio Government Degree Boys College, Qasimabad, Education and Literacy Department, Government of Sindh, Hyderabad, Pakistan. Author

DOI:

https://doi.org/10.71146/kjmr873

Keywords:

Regressions, Analysis, Workload, Characterizations, Examination, Automation, Database

Abstract

This study is focused on the workload pattern analysis of database servers running Examination Management Systems (EMS) in universities. Tests and examinations in schools and colleges are a wide range of assessment activities planned by teachers to measure, monitor, and mark the performance of students in studies, learning, and skill acquisition in a specific subject. As education administration becomes increasingly digitized, these core academic processes are now dominated by advanced, computer-based EMS systems. Therefore, it is imperative to know the workload pattern of the underlying database servers so that the performance can be optimized, the system can be made stable at peak usage hours, and future upgrades in educational technology infrastructure can be planned. This study aims to assess the workload on the database server of a Public Sector University, with a case study of the University of Sindh, Jamshoro, during official working hours. A large number of human resources with robust computer-based infrastructure are needed because of the complexity of examination process including time-table generation, attendance tracking, mark tabulation, and the issuance of academic documents (i.e., mark sheets, certificates, and degrees). In this research, statistical analysis techniques including regression are employed to evaluate system workload. In this way, the results help to enhance performance, reduce failure risk, and ensure smooth operation of the EMS.

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References

[1] References Zhibin, W., Zhang, Y., & Chen, H. (2015). Workload analysis in OLTP systems using real-time log mining. Journal of Database Systems, 32(4), 245–259.

[2] Raza, S., Ahmed, F., & Jamil, M. (2017). Role-based workload profiling for academic database systems. International Journal of Information Systems and Education, 25(2), 101–112.

[3] Brown, A., & Patterson, D. (2012). Benchmarking and regression modeling of synthetic workloads in e-commerce databases. ACM Transactions on Database Systems (TODS), 37(3), 54–70.

[4] Zhang, T., Li, X., & Wang, J. (2016). Predictive modeling of workload patterns in cloud databases using machine learning. IEEE Transactions on Cloud Computing, 4(2), 131–142.

[5] Kumar, R., & Singh, A. (2018). Visual analysis of examination server logs using heatmaps and temporal profiling. Journal of Educational Technology and Systems, 29(1), 87–95.

[6] Lee, D., Park, J., & Kim, S. (2020). Anomaly detection in healthcare databases using workload segmentation. Health Information Science and Systems, 8(3), 110–122.

[7] Alam, M., & Shah, N. (2018). Impact of academic activities on performance of student information systems: A case study. Pakistan Journal of Computer and Information Systems, 15(2), 59–68.

[8] Kumar, V., Sharma, R., & Das, A. (2020). SQL profiler-based performance assessment of academic ERP systems. Indian Journal of Information Systems, 12(4), 173–180.

[9] Sultana, R., Khan, M., & Imran, A. (2019). Temporal analysis of LMS usage in private universities. International Journal of E-Learning and Educational Technologies, 9(1), 35–44.

[10] Farooq, N., Ahmed, S., & Malik, U. (2022). Performance evaluation of examination systems using load balancing techniques. Journal of Higher Education Computing Research, 19(2), 89–101.

[11] Qureshi, Saleem Razzak, Chandio, Aftab Ahmed and Chandio, Qamar-ul-Nisa. " Characterization and Forecasting the Workload of DRLAB Medical Database Server Based on the Shift-Wise and Machine Learning Approaches." The Science tech 7, no. 1 (2026).

[12] Bhutto, Afia, Aftab Ahmed Chandio, Kirshan Kumar Luhano, and Imtiaz Ali Korejo. "Analysis of Energy and Network Cost Effectiveness of Scheduling Strategies in Datacentre." CYBERNETICS AND INFORMATION TECHNOLOGIES 23, no. 3 (2023).

[13] Jhatial, Soomal, & Chandio, Aftab Ahmed. “Analysis of Academic Web Server Traffic and Workload Characterization for Performance Evaluation.” Sindh University Research Journal - SURJ (Science Series), 55(02), 45-55. (2023) https://doi.org/10.26692/surj-ss.v55i02.6491