Optimization method of approximate aggregate query based on variational auto-encoder
An optimized variational self-encoder-based approximate aggregation query method was proposed for the problem of imbalanced distribution of biased data,which makes it difficult to sample biased distribution data with traditional approximate aggregation query methods.The effect of approximate aggregation query method on the accuracy of approximate aggregation query for biased distribution data was analyzed.The bias-distributed data were hierarchically grouped in the preprocessing stage,and the network structure and loss function of the variational self-encoder generation model were optimized to reduce the approximate aggregated query relative error.The experimental results show that the query relative error of the approximate aggregation query is smaller for skewness distribution data compared with the benchmark method,and the rising trend of the query relative error is smoother as the skewness coefficient increases.
NETL
NSTL
万方数据
维普
