TY - GEN
T1 - Design and Implementation of Business Intelligence Framework for a Global Online Retail Business
AU - Al-Omoush, Razan
AU - Fraihat, Salam
AU - Al-Naymat, Ghazi
AU - Awad, Mohammed
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Due to the intense competition in today's online retail environment, companies seek to enhance their strategies by adopting effective analytical techniques and infrastructure, allowing them to quickly analyze critical information that supports decision-making. A Business intelligence (BI) framework can promptly fulfill such needs by processing massive amounts of collected data from multiple sources and representing them in a way companies can utilize in their strategic decisions. This research paper presents a detailed design and implementation of a BI framework for the online retail business industry. It includes requirement analysis, data modeling, BI framework design, and the implementation of descriptive and predictive analytic tools to provide insights and decision support for retail businesses. Moreover, the paper details the implementation of various machine learning algorithms used in sales predictive analytics, such as Linear Regression, Lasso Regression, XGBoost, Random Forest, and LSTM. Interactive charts are provided to assist decision-makers in carrying informed decisions.
AB - Due to the intense competition in today's online retail environment, companies seek to enhance their strategies by adopting effective analytical techniques and infrastructure, allowing them to quickly analyze critical information that supports decision-making. A Business intelligence (BI) framework can promptly fulfill such needs by processing massive amounts of collected data from multiple sources and representing them in a way companies can utilize in their strategic decisions. This research paper presents a detailed design and implementation of a BI framework for the online retail business industry. It includes requirement analysis, data modeling, BI framework design, and the implementation of descriptive and predictive analytic tools to provide insights and decision support for retail businesses. Moreover, the paper details the implementation of various machine learning algorithms used in sales predictive analytics, such as Linear Regression, Lasso Regression, XGBoost, Random Forest, and LSTM. Interactive charts are provided to assist decision-makers in carrying informed decisions.
KW - Business Intelligence Architecture
KW - Dashboards
KW - Descriptive Analysis
KW - Machine Learning
KW - Online Retail
KW - Predictive Analysis
KW - Sales Prediction
UR - https://www.scopus.com/pages/publications/85146936688
U2 - 10.1109/ETCEA57049.2022.10009688
DO - 10.1109/ETCEA57049.2022.10009688
M3 - Conference contribution
AN - SCOPUS:85146936688
T3 - 2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 - Proceedings
BT - 2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022
Y2 - 23 November 2022 through 25 November 2022
ER -