Experimental Study on Machine Learning with Approximation to Data Streams

Realtime transferring of data streams enables many data analytics and machine learning applications in the areas of e.g. massive IoT and industrial automation. Big data volume of those streams is a significant burden or overhead not only to the transportation network, but also to the corresponding application servers. Therefore, researchers and scientists focus on reducing the amount of data needed to be transferred via data compressions and approximations. However, how to do data compression and approximation is highly dependent on the corresponding applications. In this paper, we did a study on the impact of data approximation to the machine learning applications. In particular, from the experimental perspective, we show the tradeoff among the approximation error bound, compression ratio and the prediction accuracy of multiple machine learning methods. We believe that, with proper choice, data approximation can dramatically reduce the amount of data transferred with limited impact on the machine learning applications.