Optimization and Application of High Performance Algorithm under Data Governance Framework

The in the era of big data and information, the optimization and application of high-performance algorithms within a Data Governance framework is crucial. However, issues arise with inaccurate performance characterization, particularly with the Grey Wolf Optimizer (GWO), which struggles to overcome phase limitations in this context, leading to suboptimal outcomes. Therefore, we present a study focused on the development and assessment of a digital mining algorithm for optimizing high-performance algorithms under a Data Governance framework in the big data environment.To initiate our investigation, we employ computational theory to identify the key factors affecting the optimization and application of high-performance algorithms under a Data Governance framework. These factors are then categorized according to the specific requirements of the optimization process, aiming to minimize the influence of extraneous variables. Computational theory is further utilized to design a digital mining algorithm tailored for the optimization and application of high-performance algorithms under a Data Governance framework. The performance of this algorithm is rigorously evaluated.Simulation results obtained using MATLAB indicate that, under specified evaluation criteria, the digital mining algorithm surpasses the traditional GWO in terms of both the accuracy of the optimization and the processing time required for influencing variable analysis.This research underscores the importance of developing advanced algorithms that can effectively navigate the complexities of data governance and big data environments, ensuring more accurate and efficient outcomes.