In general, data sets are growing larger and larger, and handling related issues is topic of big data. Similar trends and tendencies are evident in data envelopment analysis (DEA). DEA is a well-known instrument for determining the efficiencies of decision-making units (DMUs), applying linear programming. Still, as we will show, DEA suffers notably from the curse of dimensionality. Therefore, we propose improved decomposition-based algorithms involving different termination criteria and multithreading to address this issue. For some of these criteria, we prove the convergence of the algorithm; to the best of our knowledge, we are the first to prove this. Ultimately, from a computational point of view, we study the performance of the new big data strategy by an extensive numerical analysis, thus demonstrating the algorithm’s scalability.

总的来说，数据集越来越大，处理相关问题是大数据的主题。类似的趋势和趋势在数据包络分析 (DEA) 中很明显。DEA 是一种众所周知的工具，用于应用线性规划来确定决策单元 (DMU) 的效率。尽管如此，正如我们将要展示的，DEA 仍然受到维度诅咒的影响。因此，我们提出了改进的基于分解的算法，涉及不同的终止标准和多线程来解决这个问题。对于其中一些标准，我们证明了算法的收敛性；据我们所知，我们是第一个证明这一点的人。最终，从计算的角度，我们通过广泛的数值分析来研究新大数据策略的性能，从而证明算法的可扩展性。