Presently,retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and comfort.Therefore,...Presently,retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and comfort.Therefore,these stores try to accommodate all needed items for daily use or rarely required items under the same roof.However,the primary challenge for today’s retailer is that the modern customer is quality and brands conscious as well as compare for services provided to them by different outlets at the comfort of home with a single click.Therefore,customers prefer to purchase from E-Commerce websites instead of physically visiting a retail store,which leads to the downfall in the sales of retailers which become a serious threat to them.Therefore,retailers are required to work sincerely towards their customer expectations by providing all their needed goods under the same roof.Therefore,the objective of this paper is to assist retail business owners to recognize the purchasing needs of their customers and hence to entice customers to physical retail stores away from competitor E-Commerce websites.This paper employs a systematic research methodology based on association rule mining deployed over Map-Reduce based Apriori association mining and Hadoop based intelligent cloud architecture to determine useful buying patterns from purchase history of previous customers,in order to assist retail business owners.The finding acknowledges that the traditional mining algorithms have not progressed to support big data analysis as required by current retail businesses owners.The job of finding unknown association rules from big data requires a lot of resources such as memory and processing engines.Moreover,traditional mining systems are inadequate to provide support for partial failure support,extensibility,scalability etc.Therefore,this study aims to implement and develop MapReduce based Apriori(MR-Apriori)algorithm in the form of Intelligent Retail Mining Tool i.e.IRM Tool to recognize all these concerns in an efficient manner.The proposed system adequately satisfy all significant requisites anticipated from modern Big Data processing systems such as scalability,fault tolerance,partial failure support etc.Finally,this study experimentally verifies the effectiveness of the proposed algorithm i.e.MR-Apriori by speed-up,size-up,and scale-up evaluation parameters.展开更多
文摘Presently,retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and comfort.Therefore,these stores try to accommodate all needed items for daily use or rarely required items under the same roof.However,the primary challenge for today’s retailer is that the modern customer is quality and brands conscious as well as compare for services provided to them by different outlets at the comfort of home with a single click.Therefore,customers prefer to purchase from E-Commerce websites instead of physically visiting a retail store,which leads to the downfall in the sales of retailers which become a serious threat to them.Therefore,retailers are required to work sincerely towards their customer expectations by providing all their needed goods under the same roof.Therefore,the objective of this paper is to assist retail business owners to recognize the purchasing needs of their customers and hence to entice customers to physical retail stores away from competitor E-Commerce websites.This paper employs a systematic research methodology based on association rule mining deployed over Map-Reduce based Apriori association mining and Hadoop based intelligent cloud architecture to determine useful buying patterns from purchase history of previous customers,in order to assist retail business owners.The finding acknowledges that the traditional mining algorithms have not progressed to support big data analysis as required by current retail businesses owners.The job of finding unknown association rules from big data requires a lot of resources such as memory and processing engines.Moreover,traditional mining systems are inadequate to provide support for partial failure support,extensibility,scalability etc.Therefore,this study aims to implement and develop MapReduce based Apriori(MR-Apriori)algorithm in the form of Intelligent Retail Mining Tool i.e.IRM Tool to recognize all these concerns in an efficient manner.The proposed system adequately satisfy all significant requisites anticipated from modern Big Data processing systems such as scalability,fault tolerance,partial failure support etc.Finally,this study experimentally verifies the effectiveness of the proposed algorithm i.e.MR-Apriori by speed-up,size-up,and scale-up evaluation parameters.
