As more than 2.5 quintillion bytes of data are generated every day, the era of big data is undoubtedly upon us. Running analysis on extensive datasets is a challenge. Fortunately, a significant percentage of the data accrued can be omitted while maintaining a certain quality of statistical inference in many cases. Censoring provides us a natural option for data reduction. However, the data chosen by censoring occur nonuniformly, which may not relieve the computational resource requirement. In this paper, we propose a dynamic, queuing method to smooth out the data processing without sacrificing the convergence performance of censoring. The proposed method entails simple, closed-form updates, and has no loss in terms of accuracy comparing to the original adaptive censoring method.Simulation results validate its effectiveness.
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