NONE 20260610115723180 wseas/wseas content-registration-form+ja@crossref.org content-registration-form International Journal of Computational and Applied Mathematics & Computer Science 2769-2477 Hassan Bediar Hashim Middle Technical University Baghdad IRAQ In the context of Relational Database Management Systems (RDBMSs), we investigate how data size and index strategies affect query response time. With the explosive increase of data, efficient data processing and user satisfaction are highly dependent on the performance of database. Response Time is the major indicator of the performance being experienced. The study is conducted with an analytical method through performance evaluation experiments in which query execution times are measured for several size datasets with indexed and non-indexed configurations (none, B-Tree, Hash). Anyway, for large dataset, we would take Cost-Based Optimization (CBO) into consideration the optimize execution plans (i.e., adaptive or learned indexing). The response time for the above query is a strong function of data size (positive correlation) and degradation is large in case of no indexing. Correct indexing can hugely reduce response time; we estimate at a figure of around 35– 60%. ((Note that B-Tree indexing works comparably or better on various workloads: response times for heavy workloads are 40–45% baseline on B-Tree as opposed to 55–60% in the case of Hash.) This process highlights the importance of proper indexing strategies for both performance and scalability, as well has having useful implications on database design principles as well in data-intensive applications. 06 10 2026 06 10 2026 33 4 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232028.2026.6.4 https://wseas.com/journals/camcs/2026/a08camcs-004(2026).pdf Saidu, I.C., Yusuf, M., Nemariyi, F.C., George, A.C.: Indexing techniques and structured queries for relational database management systems. Journal of the Nigerian Society of Physical Sciences 6, 2155–2163 (2024) Mohammed, A.S.: Dynamic data: Achieving timely updates in vector stores. Libertatem Media Private Limited (2024) Dritsas, E., Trigka, M.: A survey on database systems in the big data era: Architectures, performance, and open challenges. IEEE Access 13, 1–25 (2025) Zulkifli, A.: Accelerating database efficiency in complex IT infrastructures: Advanced techniques for optimizing performance, scalability, and data management in distributed systems. International Journal of Information and Cybersecurity 7(12), 81–100 (2023) Sar, A., Choudhury, T., Sati, S., Joshi, P., Aich, S., Pant, B., Dewangan, B.K.: A comparative study and analysis of optimized indexing algorithms in database management systems. In: Proc. OPJU Int. Technology Conf. (OTCON), pp. 1–7. IEEE (2024) Robinson, E., Anderson, J.: Comparative study of adaptive indexing techniques for performance improvement in dynamic workloads. Journal of Innovation in Governance and Business Practices 1(1), 32–58 (2025) Abbasi, M., Bernardo, M.V., Váz, P., Silva, J., Martins, P.: Revisiting database indexing for parallel and accelerated computing: A comprehensive study and novel approaches. Information 15(8), 429 (2024) Raveh, E., Ofek, Y., Bekkerman, R., Cohen, H.: Applying big data visualization to detect trends in performance reports. Evaluation 26(4), 516– 540 (2020) Uzzaman, A., Jim, M.M.I., Nishat, N., Nahar, J.: Optimizing SQL databases for big data workloads: Techniques and best practices. Academic Journal on Business Administration, Innovation & Sustainability 4(3), 15–29 (2024) Murarka, S., Jain, A., Singh, L.: Advanced techniques in data ingestion and pipelining for scalable big data platforms. In: Proc. IEEE ICTBIG, pp. 1–6 (2024) Sappa, A.: Neural network powered indexing techniques for high performance data retrieval. Research Briefs on Information and Communication Technology Evolution 11, 22– 41 (2025) Ramu, V.B.: Optimizing database performance: Strategies for efficient query execution and resource utilization. International Journal of Computer Trends and Technology 71(7), 15–21 (2023) Sundarakumar, M.R., et al.: Performance tuning and scalability in big data analytics. Journal of Intelligent & Fuzzy Systems 44(3), 5231–5255 (2023) Yang, X.: Improving relevance and speed of semantic search through database indexing. In: Optimization Algorithms – Classics and Recent Advances (2023) Bhosale, P.: NoSQL databases explored: Columnar, graph, and document-based approaches. North American Journal of Engineering Research 3(4) (2022) Ullah, I., Alam, S., Ali, Z., Khan, M., Jabeen, F., Khusro, S.: Current state of query formulation for search systems. Artificial Intelligence Review 56(10), 12085–12130 (2023) Parsaei, E., Mirzabeigi, M., Sotudeh, H.: Factors affecting query formulation: A systematic review. Librarianship and Information Organization Studies 33(2), 37–53 (2022) Rahman, M.M., Islam, S., Kamruzzaman, M., Joy, Z.H.: Advanced query optimization in SQL databases for real-time analytics. Academic Journal on Business Administration, Innovation & Sustainability 4(3), 1–14 (2024)