A comprehensive review from sequential association computing to Hadoop-MapReduce parallel computing in a retail scenario. Issue 4 (2nd October 2017) - Verma, Neha; Singh, Jatinder
Abstract : Today, the customer's requirements are entirely transformed. Many big retail organizations are facing sudden decline in the sales and revenues caused due to indecisive and erratic purchasing habits of recent generation of users, as they get abundant preferred information such as cheaper rates, amazing offers, discounts, comparison of similar products, etc. over their smartphones or laptops hence they straightaway place order instead of walking down to showroom. As a result, large companies such as Tesco, Wal-Mart, Target, etc. have realized that it is requisite to shake hands with startup firms which already supports platform to retain customers either via deep exploration of transactional data or by offering lucrative offers in the benefit of customer and to promote market basket. The data which are generated from consumer purchase pattern, Big Data is a concern for companies as a result various big retail organizations are applying advanced and scalable data mining algorithms to precisely store and evaluate data in real-time manner to boost market basket analysis. This research work discusses various improved association rule mining (ARM) algorithms. The objective of this study is to identify gaps, providing opportunities for new research, to recognize expansion of Big Data analytics with retail environment and its future directions. This paper assimilates various aspects of parallel ARM algorithm for market basket analysis against sequential and distributed nature which are further escalated to Hadoop and MapReduce computing platform. Further various use cases highlighting the need of 'Big Data Retail Analytics' are discussed for emerging trends to promote sales and revenues, to keep check on competitor's websites, comparison of various brands, enticing new customers.