Development of Distributed Search Algorithm for Improving Efficiency in Web Search Engines

Abstract

This research article presents the development of a distributed search algorithm aimed at enhancing the efficiency of web search engines. Through a qualitative methodology involving literature review and library research, the study seeks to address the growing demand for improved search engine performance in handling large-scale data while maintaining fast response times.

The literature review explores existing search algorithms and their limitations in processing vast amounts of information distributed across various servers. By analyzing prior research, the article identifies the need for a distributed approach to optimize search engine functionality and reduce latency.

The developed distributed search algorithm employs parallel processing techniques to distribute search queries across multiple nodes, thereby improving overall system efficiency and response time. By leveraging distributed computing resources, the algorithm enhances the scalability and reliability of web search engines, enabling them to handle increased user traffic and data volume.

The findings underscore the effectiveness of the proposed algorithm in significantly reducing search latency and improving search engine performance, as evidenced by experimental results. Furthermore, the article discusses potential applications of the distributed search algorithm in various domains, including e-commerce, information retrieval, and data analytics.