Scalable Analytics Model Calibration with Online Aggregation

Model calibration is a major challenge faced by the plethora of statistical analytics packages that are in-creasingly used in Big Data applications. Identifying the optimal model parameters is a time-consuming process that has to be executed from scratch for every dataset/model combination even by experienced data scientists. We argue that the lack of support to quickly identify sub-optimal configurations is the principal cause. In this paper, we apply parallel online aggregation to identify sub-optimal configura-tions early in the processing by incrementally sampling the training dataset and estimating the objective function corresponding to each configuration. We design concurrent online aggregation estimators and define halting conditions to accurately and timely stop the execution. The end-result is online approxi-mate gradient descent—a novel optimization method for scalable model calibration. We show how online approximate gradient descent can be represented as generic database aggregation and implement the resulting solution in GLADE—a state-of-the-art Big Data analytics system