Authors:
Hlaing Htake Khaung Tin, Loay Sherif Elsayed Mohamed Tohamy, Marawan Soliman Abdelmonem Madni Soliman, Lamar Adel Khalil Abuelfadl, Jana Amr Abdelsamee Issa Ibrahim, Ismail Ahmed Ismail Youssef Abdelmoaty
Addresses:
Faculty of Information Science, University of Information Technology, Yangon, Myanmar. Department of Biomedical Sciences, Zewail City of Science and Technology, 6th of October City, Giza Governorate, Egypt.
Abstract:
Methods of making decisions based on data have become essential in enhancing performance and achieving competitive advantage among modern business organisations. While Business Intelligence (BI) can offer sophisticated ways to collect, process, and present data, it cannot leverage domain knowledge or engage in intelligent reasoning. The role of Knowledge Engineering (KE) is to add knowledge representation, business rules, and inference to the decision-making process. This paper proposes a framework that combines BI and KE to develop intelligent decision-support systems and enable explainable, intelligent business decisions. The proposed system is seen as a four-layer structure comprising a data management layer, a Business Intelligence (BI) analytics layer, a knowledge base and an inference engine. The data management layer is responsible for storing and retrieving necessary organisational data. The BI analytics layer processes this data to produce relevant analytical findings such as trends, indicators and performance statistics. The knowledge base consists of business rules, organisational policies, expert knowledge and specified decision criteria. Finally, the inference engine combines the analytical findings with relevant knowledge and business rules to provide appropriate suggestions and decision assistance. A retail business case involving customer segmentation, inventory management and business planning is used to demonstrate the applicability of the suggested framework and to show how integrated analytical and knowledge-based reasoning can support multiple organisational decision-making activities.
Keywords: Business Intelligence; Knowledge Engineering; Data-Driven Decision Making; Decision Support System; Knowledge Base; Inference Engine; Business Analytics; Intelligent Systems.
Received on: 28/01/2025, Revised on: 27/03/2025, Accepted on: 08/06/2025, Published on: 05/06/2026
DOI: 10.64091/ATICL.2026.000328
AVE Trends in Intelligent Computer Letters, 2026 Vol. 2 No. 2 , Pages: 100-110