Novel QoS Load Shedding Architecture Based on AR* -GRNN
It is important to obtain effective feature values of data stream and forecast them in overload system for mining data stream,because data streams are often bursty and data characteristic vary over time.In this paper,we proposed a load shedding framework based on DFT and AR* -GRNN for similarity search on data stream,which is constructed to achieve minimized mining loss.In this load shedding framework,we introduce Discrete Fourier transform(DFT) technology to obtain feature values using fewer coefficients.Autoregressive class model(AR*)-Generalized Regression Neural Network(GRNN) model is used to forecast the feature values of which the data streams are shed,and we perform similarity search using these forecasting values.Experimental results indicate that the proposed method is an effective method in improving query quality when the system is under overload situation.