An intelligent approach to design big data on e-commerce in cloud computing environment

Abstract

Web resources extract useful knowledge by the process of web mining. Web server maintains the log files for analyzing them from behavior of customer and improves business as the challenging task for E-commerce companies. The processing and computing of big data was increased day by day by the demand of computer system’s ability. The emphasis on data was increased gradually by the rapid development of information technology. Various businesses are exploring effective data analysis methods, and this system proposes an intelligent approach to designing big data for e-commerce in a cloud computing environment. This paper aims to develop and implement the relevancy vector (RV) algorithm, an innovative page ranking algorithm based on Hadoop distributed file system (HDFS) map reduce. The research provides customers with a robust meta search tool that makes it easy for them to understand personalized search requirements and make purchases based on their preferences. The intelligent meta search system adverse events (IMSS-AE) tool and the RV page ranking algorithm were shown to be efficient and effective by a thorough experimental evaluation in terms of reduced response time, enhanced page freshness, high personalized relevance, and high hit rates.