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Cited by 4Advanced Database Systems and QueriesCloud Computing and Resource ManagementImpact of AI and Big Data on Business and Society

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Oracle Autonomous Database for Transforming Database Administration by Harnessing Artificial Intelligence

Avula, Suresh Babu·2025·2025 Global Conference in Emerging Technology (GINOTECH)
DOI 10.1109/ginotech63460.2025.11076743
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Abstract

In the modern era of big data and cloud computing, efficient database management systems (DBMS) handle vast amounts of structured and unstructured data in organizations. Traditional database administration requires continuous human intervention for performance tuning, security patching and resource scaling, leading to high operational costs and potential human errors. Oracle Autonomous Database (OAD) addresses these challenges by utilizing Artificial Intelligence (AI) and Machine Learning (ML) to automate key administrative tasks, enabling self-driving, self-securing and self-repairing capabilities. This study introduces a Reinforcement Learning (RL)-based approach for optimizing query execution in OAD. The proposed RL model dynamically adapts to workload variations, minimizing execution time and optimizing resource utilization. Experimental results validate the effectiveness of RL-based query optimization, showing a 38.9% reduction in execution time from an average of 1.8 seconds to 1.1 seconds per query. Resource consumption metrics also improved significantly, with CPU usage decreasing by 27.1%, memory consumption reducing by 23.3% and disk I/O operations improving by 31.4%. Additionally, query throughput increased by 40% demonstrating enhanced database efficiency. These findings highlight the transformative impact of AI-driven automation in database administration, making OAD a viable solution for enterprises seeking efficiency, cost savings and high-performance data mana…

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Research works printed but not linked: 5

  1. Mullins, 2002, Database administration: the complete guide to practices and procedures
  2. Pulivarthy, 2023, Enhancing Database Query Efficiency: AI-Driven NLP Integration in Oracle, Transactions on Latest Trends in Artificial Intelligence, 4
  3. Schwertner, 2021, Public and private autonomous data base cloud, Electrotechnica & Electronica (E+ E), 56
  4. Chinta, 2021, HARNESSING ORACLE CLOUD INFRASTRUCTURE FOR SCALABLE AI SOLUTIONS: A STUDY ON PERFORMANCE AND COST EFFICIENCY, Technix International Journal for Engineering Research, 8, a29
  5. Teleaşă, Oracle Machine Learning for Python in APEX-Analyzing and Predicting CO2 Emission by private vehicles, DATABASE SYSTEMS, 47

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Metadata

Authors
Avula, S. B.
Affiliations
Associate Director Databases,India
Corresponding author
Avula, Suresh Babu
Venue
2025 Global Conference in Emerging Technology (GINOTECH)
Publisher
IEEE
Published
2025-05-09
Pages
1-7
DOI
10.1109/ginotech63460.2025.11076743
OpenAlex
W4413068212
Cited by
1
References
11
Citations by year
2025: 1
FWCI
1.25
Type
conference paper
Language
en
Open access
closed
Licence
Locations
Global Conference in Emerging Technology (GINOTECH) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
Keywords
reinforcement learning, query optimization, query execution time, resource utilization, workload adaptation, query throughput

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CoChat. (2026). Citation check: Oracle Autonomous Database for Transforming Database Administration by Harnessing Artificial Intelligence. https://app.cochat.ai/papers/10.1109/ginotech63460.2025.11076743
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Avula, S. B. (2025). Oracle Autonomous Database for Transforming Database Administration by Harnessing Artificial Intelligence. 2025 Global Conference in Emerging Technology (GINOTECH). https://doi.org/10.1109/ginotech63460.2025.11076743