Study of A Streamlining Method Based on Big Data

In The data with high sampling rate has a lot of redundancy. In order to speed up the operation and reduce the storage and computation of the data, a streamlined algorithm is proposed in this paper. The algorithm extracts all original sampling points, so as to preserve the trend of data as much as possible and ensure the authenticity of the data. Simulation results show that algorithm overcomes the defect of extreme point extraction method with too large errors, which not only ensures the precision of data fitting, but also reduces the amount of calculation significantly. The method is simple to operate and easy to use for engineering.