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2025
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| Accesso online: | https://doi.org/10.5281/zenodo.15149967 |
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| _version_ | 1866902206381293568 |
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| author | Kundan Kumar , Dr. Nandeshwar Prasad Singh , Dr. Arif Md. Sattar , Mritunjay Kr. Ranjan |
| author_facet | Kundan Kumar , Dr. Nandeshwar Prasad Singh , Dr. Arif Md. Sattar , Mritunjay Kr. Ranjan |
| contents | <p><strong>Abstract:- </strong>It is now possible to leverage cloud-based databases in storing and managing data due to the advantages that it<br>has that includes scalability, flexibility, not to mention the issue of cost. However, the issue of enhancing query<br>performance in these systems is far from trivial because of the constantly changing workload, network delay<br>time, competition for resources, and data dispersion. This paper discusses the main issues connected with query<br>optimization in cloud settings and brings an overview of the existing methods to improve it. After that, we<br>discuss conventional as well as advanced forms of optimization and some of them are indexing, caching,<br>partitioning, creating materialized views and query rewriting. Moreover, we explore the recent strategies that<br>utilize machine learning algorithm, adaptivity in query processing and workload-awareness for enhancing the<br>query execution. The work also pragmatic on the role of distributed query execution profile, multi-clouds and<br>serverless architectures. In this paper, the author tries to classify new techniques and modern trends to identify<br>the methods that actually help to optimize the query performance and eliminate the emerging bottlenecks in<br>cloud databases. Theoretical and methodological contributions of the findings; the study contributes to the<br>enhancement of optimization approaches regarding response time of cloud-based database systems and the<br>proportionate use of resources needed for such systems.<br><strong>Keywords:-</strong> Query Optimization, Cloud Databases, Indexing, Machine Learning, Distributed Query Processing, Adaptive Query Execution</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15149967 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | Cloud Query Alchemy: Transforming Database Performance with Next-Gen Optimization Techniques Kundan Kumar , Dr. Nandeshwar Prasad Singh , Dr. Arif Md. Sattar , Mritunjay Kr. Ranjan <p><strong>Abstract:- </strong>It is now possible to leverage cloud-based databases in storing and managing data due to the advantages that it<br>has that includes scalability, flexibility, not to mention the issue of cost. However, the issue of enhancing query<br>performance in these systems is far from trivial because of the constantly changing workload, network delay<br>time, competition for resources, and data dispersion. This paper discusses the main issues connected with query<br>optimization in cloud settings and brings an overview of the existing methods to improve it. After that, we<br>discuss conventional as well as advanced forms of optimization and some of them are indexing, caching,<br>partitioning, creating materialized views and query rewriting. Moreover, we explore the recent strategies that<br>utilize machine learning algorithm, adaptivity in query processing and workload-awareness for enhancing the<br>query execution. The work also pragmatic on the role of distributed query execution profile, multi-clouds and<br>serverless architectures. In this paper, the author tries to classify new techniques and modern trends to identify<br>the methods that actually help to optimize the query performance and eliminate the emerging bottlenecks in<br>cloud databases. Theoretical and methodological contributions of the findings; the study contributes to the<br>enhancement of optimization approaches regarding response time of cloud-based database systems and the<br>proportionate use of resources needed for such systems.<br><strong>Keywords:-</strong> Query Optimization, Cloud Databases, Indexing, Machine Learning, Distributed Query Processing, Adaptive Query Execution</p> |
| title | Cloud Query Alchemy: Transforming Database Performance with Next-Gen Optimization Techniques |
| url | https://doi.org/10.5281/zenodo.15149967 |