Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2025
|
| Online Access: | https://doi.org/10.5281/zenodo.15149967 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of 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>