Simultaneously processing several large blocks of streaming data is a computationally expensive problem. Based on the incremental singular value decomposition algorithm, we propose a new procedure for calculating the factorization of the multiblock redundancy matrix \({{\textbf {M}}}\), which makes the multiblock method more fast and efficient when analyzing large streaming data and high-dimensional dense matrices. The procedure transforms a big data problem into a small one by processing small high-dimensional matrices where variables are in rows. Numerical experiments illustrate the accuracy and performance of the incremental solution for analyzing streaming multiblock redundancy data. The experiments demonstrate that the incremental algorithm may decompose a large matrix with a 75% reduction in execution time. It is more efficient to first partition the matrix \({{\textbf {M}}}\) and then decompose it with the incremental algorithm than to decompose the entire matrix \({{\textbf {M}}}\) using the standard singular value decomposition algorithm.

同时处理几个大块的流数据是一个计算成本高昂的问题。基于增量奇异值分解算法，我们提出了一种计算多块冗余矩阵\({{\textbf {M}}}\)因式分解的新程序，使得多块方法在分析大流时更加快速和高效数据和高维密集矩阵。该过程通过处理变量按行排列的小型高维矩阵，将大数据问题转化为小数据问题。数值实验说明了用于分析流式多块冗余数据的增量解决方案的准确性和性能。实验表明，增量算法可以分解大矩阵，同时减少 75% 的执行时间。首先对矩阵\({{\textbf {M}}}\)进行分区，然后使用增量算法进行分解，比使用分解整个矩阵\ ({{\textbf {M}}}\) 的效率更高标准奇异值分解算法。