Abstract
Cloud computing has gained significant traction due to its cost-effectiveness, scalability, and flexible pay-as-you-go model—trends further accelerated by the proliferation of IoT devices. As a result, many organizations now leverage Database-as-a-Service (DBaaS) for database deployment, enjoying benefits such as high availability, automated scaling, failover support, and reduced administrative overhead. This paper enhances the performance of shared cloud databases under mixed transactional and analytical workloads through two complementary strategies: Business intelligence and decision support systems often execute complex queries with multiple joins and aggregations. To minimize repeated computations, a scalable tree-mining algorithm is employed to identify frequently occurring sub-expressions from historical query plans. These are materialized as views, significantly lowering query execution costs. Web applications like e-commerce and online banking experience region-specific, time-dependent access patterns. To address this, a predictive model based on Parzen window estimation is used to identify time-varying working sets. A novel cache replacement strategy is then proposed, prioritizing blocks based on predicted reuse. Experimental evaluations demonstrate that the proposed methods significantly improve cache hit rates and overall performance compared to existing solutions.
Citations
IRE Journals:
Rajeev Maheshwari , Rajeev Kaushik
"Enhancing Database Management System Performance Using Data Mining Techniques" Iconic Research And Engineering Journals Volume 9 Issue 1 2025 Page 1316-1319
IEEE:
Rajeev Maheshwari , Rajeev Kaushik
"Enhancing Database Management System Performance Using Data Mining Techniques" Iconic Research And Engineering Journals, 9(1)
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