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Decision support systems (DSS) play a pivotal role as computerized tools, guiding and enhancing decision-making processes vital for organizational progress. This research focuses on developing a system tailored for dynamic decision-making, particularly emphasizing the integration of data mining technology. Decision algorithms and neural networks are discussed in depth, providing a comprehensive understanding of the analytical tools crucial for effective decision support. Additionally, the research sheds light on potential risks, ensuring a nuanced view of challenges that may impact the development of DSS. A significant portion of the study is dedicated to the design of DSS architecture and the strategic integration of data mining within the database. The proposed development stages for a business information system, ranging from feasibility study to release, serve as a structured framework for practical implementation. Details within each stage, including data analysis, cleaning, and module development, are meticulously examined. Emphasis is placed on critical steps such as system design, database design, and extract, transform, load (ETL) process design, elucidating their importance in the holistic development of DSS. The conclusion reinforces the paramount importance of leveraging data mining technology in the process of developing decision support systems.
Asgarova et al. (Fri,) studied this question.