Dominant Features Selection with Clustering Genetic Model to Improve the Access Time of Data in Big Data Management Using Distributed Machine Learning
Dominant Features Selection with Clustering Genetic Model to Improve the Access Time of Data in Big Data Management Using Distributed Machine Learning |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-3 |
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| Year of Publication : 2025 | ||
| Author : Peerzada Hamid Ahmad, Munishwar Rai |
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| DOI : 10.14445/22315381/IJETT-V73I3P128 | ||
How to Cite?
Peerzada Hamid Ahmad, Munishwar Rai, "Dominant Features Selection with Clustering Genetic Model to Improve the Access Time of Data in Big Data Management Using Distributed Machine Learning," International Journal of Engineering Trends and Technology, vol. 73, no. 3, pp. 403-422, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I3P128
Abstract
The explosive nature of big data has created serious challenges for information managers, especially in providing fast availability and response times. Conventional data management systems tend to falter when dealing with enormous datasets, which causes latency that can slow down real-time analysis and decision-making. In response, this research introduces a new cluster-based genetic model aimed at hastening access to data in big data management systems. The method combines a genetic model with an emphasis on feature selection to maximize data retrieval speed. Through the use of distributed machine learning techniques, the model detects and ranks the most significant features, optimizing the clustering process to minimize access time and retrieval complexity. The genetic method reduces access time and increases clustering efficiency by focusing on prominent features. An evolutionary algorithm is used to optimize data storage and retrieval in such a way as to minimize retrieval times. The research tackles crucial issues like the requirement for high-speed data processing, data system scalability, and data structure complexity. The proposed model adapts dynamically to the changing data landscape, reducing latency and improving the overall efficiency of large-scale data systems. Results show that the cluster-based genetic model greatly enhances data access efficiency. It recorded a 35% decrease in access time when tested on large datasets compared to traditional data management methods. The median data retrieval time was reduced from 120 milliseconds to 78 milliseconds, showing the model's efficiency in optimizing data clustering and retrieval processes. This decrease in access time showcases the model's ability to optimize the efficiency of big data systems, especially in situations that involve quick and efficient data retrieval.
Keywords
Big Data Management, Cluster-Based Genetic Model, Data Access Time, Data Retrieval Efficiency, Distributed Machine Learning, Real-Time Processing, Scalability.
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