Agent-based Big Data Analytics in Retailing: A Case Study

• Main Authors: Firas, D. Ahmed, Aws Naser, Jaber, Mazlina, Abdul Majid, Mohd Sharifuddin, Ahmad
• Format: Conference or Workshop Item
• Language: English; English
• Published: IEEE 2015
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/11814/1/Agent-based%20Big%20Data%20Analytics%20in%20retailing-%20A%20case%20study.pdf; http://umpir.ump.edu.my/id/eprint/11814/7/Agent-based%20Big%20Data%20Analytics%20in%20retailing-%20A%20case%20study-abstract.pdf; http://umpir.ump.edu.my/id/eprint/11814/; http://dx.doi.org/10.1109/ICSECS.2015.7333085
• Institution: Universiti Malaysia Pahang

The advent of social networks and the Internet of Things have created massive data sets with huge and complex structures. Thus, new technology for storage, analysis, and pattern visualization must be developed for further processing. Such data sets are appropriately termed as “Big Data.” Big data Analytics is concerned with exposing and visualizing hidden patterns, as well as with analyzing the knowledge that is produced to facilitate decision making. In retailing, analyzing the massive data generated from business transactions is crucial to enhancing the insights of vendors into consumer behaviors and purchases, thus providing them an advantage in decision making. The capability to extract value from big data is a relevant issue, but the process is difficult as the volume and velocity of data increase. As a result, traditional business intelligence methods become inadequate. Consequently, we propose an agent-based paradigm in this study to facilitate the use of Big Data Analytics in retailing. The paradigm exploits agent characteristics such as autonomy, pro-activity, and intelligence in performing data analytics processes. We also review the background of the situation and discuss the characteristics, properties, applications, and challenges of integrating Big Data with multi-agent systems in retailing.