Publication:
Agent-based Big Data Analytics in retailing: A case study

Simple item page
dc.citedby5
dc.contributor.authorAhmed F.D.en_US
dc.contributor.authorJaber A.N.en_US
dc.contributor.authorMajid M.B.A.en_US
dc.contributor.authorAhmad M.S.en_US
dc.contributor.authorid57188658131en_US
dc.contributor.authorid54934197600en_US
dc.contributor.authorid57222473453en_US
dc.contributor.authorid56036880900en_US
dc.date.accessioned2023-05-29T05:59:39Z
dc.date.available2023-05-29T05:59:39Z
dc.date.issued2015
dc.descriptionAutonomous agents; Complex networks; Computer software; Consumer behavior; Data handling; Data mining; Decision making; Digital storage; Intelligent agents; Metadata; Multi agent systems; Software engineering; Business transaction; Complex structure; Data analytics; Hidden patterns; Industrial automation; Massive data sets; Pattern visualization; Retail; Big dataen_US
dc.description.abstractThe 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. � 2015 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7333085
dc.identifier.doi10.1109/ICSECS.2015.7333085
dc.identifier.epage72
dc.identifier.scopus2-s2.0-84962045593
dc.identifier.spage67
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84962045593&doi=10.1109%2fICSECS.2015.7333085&partnerID=40&md5=53fd0b40c907b2ebc31247d77e3b8edc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22215
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2015 4th International Conference on Software Engineering and Computer Systems, ICSECS 2015: Virtuous Software Solutions for Big Data
dc.titleAgent-based Big Data Analytics in retailing: A case studyen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS