Grebenisan Gavril, University of Oradea, Romania

Performing full-text searches on extensive datasets typically generates large index sizes, which can significantly burden computer systems by consuming a considerable amount of CPU time and disk bandwidth. This heavy consumption can lead to performance bottlenecks, slow response times, and increased operational costs, making it imperative to find effective solutions to enhance processing efficiency. To tackle these challenges, it is essential to develop a sophisticated architecture tailored for query processing systems that can manage these high demands. In this paper, we introduce a comprehensive server clustering architecture designed specifically for the rapid and efficient processing of full-text searches on enormous volumes of web data. This architecture employs a distributed system of servers that work collaboratively to handle search queries, allowing for workload balancing and parallel processing. By clustering servers together, we can optimize resource allocation, reduce response times, and improve overall throughput. Our approach not only streamlines the search process but also aims to enhance the user experience by ensuring prompt and accurate search results, even when dealing with massive datasets. Through our research, we illustrate the effectiveness of this architecture in mitigating resource consumption while scaling efficiently to meet the demands of modern data-intensive applications.

APA:
Gavril, G. (2023). A Server Clustering Approach for Conducting Extensive Full-Text Searches Across Massive Datasets on the Web. Journal of Science and Engineering Management, 4(2), 1-12. https://doi.org/10.33832/jsem.2023.4.2.01