Heuristic Approach for Optimized Data Extraction Towards Relational Databases Using Reinforcement Learning Approach

  • Sruthi Mol. A , Victor S.P
Keywords: data extraction, relational database, reinforcement learning, optimality, data retrieval

Abstract

The effective way of accessing the data from relational database is far superior than the normal databases in which the relational databases adopts more advanced technical feasibility than with the earlier databases. Data extraction in relational databases is handled easier with the proper utilization of tools and information's in order to save time and space. The aim and objective of this article focuses on Data Extraction with optimal clarity and accuracy is the primary objectives towards the aim of time saving retrieval of data from relational databases. The proposed operation of this article deals with the proper utilization of reinforcement learning approach implementation towards the accuracy and clarity in relational database extraction schema with improved strategies in iteration for fast accessing to acquire the expected results. The optimal results in the process of extracting data from real-time relational databases use the reinforcement learning approach for the accuracy and time saves strategies towards expected output attainments. The combination of neural reinforcement in relational database extraction procedures is improving the efficiency in terms of accuracy. In future this paper will be extended by implementing the fuzzy logic based genetic algorithm approach for extracting the repeated resultant sets for minimizing the time of response for maximizing the gain in the relational database access performance.

Published
2021-09-01
How to Cite
Sruthi Mol. A , Victor S.P. (2021). Heuristic Approach for Optimized Data Extraction Towards Relational Databases Using Reinforcement Learning Approach . Design Engineering, 5061-5071. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/3904
Section
Articles