Research on Optimization of Decision Support System Integrating Big Data and Machine Learning

Owing to the latest innovations in big data technologies and progress of artificial intelligent modalities a number of fields have gone through a revolutionized decision making system. In this study research, the authors provide an optimal architecture of Decision Support System that appears to effectively integrate the big data analytics with the sophisticated machine learning algorithms to improve the decision making processes. The proposed system shares with big data platforms, a foundation of flexible and efficient technological solutions for handling large datasets. This is done through the use of machine learning algorithms for data prediction, data pattern recognition, and data decision making in real-time data bases. They highlighted that the optimization of the DSS architecture is another innovation of this research since it would lead to the enhancement of the accuracy, minimization of the latency and the user interface. To accomplish this research work to its best, numerous simulations of the system and real-case scenarios were carried out to evaluate the efficacy of the developed optimized system. These findings clearly illustrate increased levels of decision precision as well as decision-making speed. This research provides a sound solution for organizations that want to optimize the use of big data and incorporate machine learning methods into decision-making processes resulting in more savvy decisions.