An intelligent approach to Big Data analytics for sustainable retail environment using Apriori-MapReduce framework

Purpose - The purpose of this paper is to explore various limitations of conventional mining systems in extracting useful buying patterns from retail transactional databases flooded with Big Data. The key objective is to assist retail business owners to better understand the purchase needs of their customers and hence to attract customers to physical retail stores away from competitor e-commerce websites. Design/methodology/approach - This paper employs a systematic and category-based review of relevant literature to explore the challenges possessed by Big Data for retail industry followed by discussion and implementation of association between MapReduce based Apriori association mining and Hadoop-based intelligent cloud architecture. Findings - The findings reveal that conventional mining algorithms have not evolved to support Big Data analysis as required by modern retail businesses. They require a lot of resources such as memory and computational engines. This study aims to develop MR-Apriori algorithm in the form of IRM tool to address all these issues in an efficient manner. Research limitations/implications - The paper suggests that a lot of research is yet to be done in market basket analysis, if full potential of cloud-based Big Data framework is required to be utilized. Originality/value - This research arms the retail business owners with innovative IRM tool to easily extract comprehensive knowledge of useful buying patterns of customers to increase profits. This study experimentally verifies the effectiveness of proposed algorithm.