Intelligent Decision Degradation Analysis under Extreme Data Scalability in Enterprise Information Systems

Abstract

This paper provides a computer framework describing a research on the deterioration of the decision making of AI based enterprise information systems in reaction to the extreme data scale issues. The pattern of erosion of the decision-making accuracy of four AI architectures, that is, neural networks, random forests, support vector machines, and ensemble approaches, was studied by means of controlled multi-scenario simulation. These experiments were performed in four cases of scalability of linear growth, exponential burst, step-wise expansion and random volatility.

One of the most important innovations in our approach is a simulation engine which will be employed in order to measure degradation using various performance measures i.e. decision accuracy, response time, uniformity and computational load. We marked important threshold limits in our analysis, where the reliability of the system will be very low and where an early warning of the looming collapse would be detected. We also came up with predictive models that we used to predict degradation patterns.

The results show that the ensemble methods are much stronger with the average accuracy of 94.3 percent even in extreme stress conditions, which are not present in traditional architectures. On the other hand, exponential bursts exhibit the greatest performance disparities, and after their thresholds are exceeded, at least 25 performances are required to initiate execution. Finally, this work provides new techniques for assessing recovery dynamics, resilience, and decay rates. We provide organizations that are having trouble simplifying their architectures with practical, hands-on guidance. The most important lessons are to maintain decision-making for workloads involving a lot of data by offering proactive system layout policies. The established threshold detection and preemptive intervention techniques are part of a new paradigm of predictive system management that we have developed.