In the big data era researchers face a series of problems. Even standard approaches/methodologies, like linear regression, can be difficult or problematic with huge volumes of data. Traditional approaches for regression in big datasets may suffer due to the large sample size, since they involve inverting huge data matrices or even because the data cannot fit to the memory. Proposed approaches are based on selecting representative subdata to run the regression. Existing approaches select the subdata using information criteria and/or properties from orthogonal arrays. In the present paper we improve existing algorithms providing a new algorithm that is based on D-optimality approach. We provide simulation evidence for its performance. Evidence about the parameters of the proposed algorithm is also provided in order to clarify the trade-offs between execution time and information gain. Real data applications are also provided.

翻译：在大数据时代，研究者面临一系列问题。即便是线性回归等标准方法/技术，在处理海量数据时也可能变得困难或存在挑战。大数据集中回归的传统方法可能因样本量庞大而受到影响，这涉及对大型数据矩阵求逆，甚至数据无法完全载入内存。现有方法基于选取代表性子数据进行回归，它们利用信息准则和/或正交数组的性质来选取子数据。本文改进了现有算法，提出了一种基于D-最优性方法的新算法。我们通过模拟实验验证了其性能，同时提供了算法参数的相关证据，以阐明执行时间与信息增益之间的权衡关系。此外，还开展了实际数据应用验证。