Presentation
Error-controlled Progressive Retrieval of Scientific Data under Derivable Quantities of Interest
DescriptionThe unprecedented amount of scientific data has introduced heavy pressure on the data storage and transmission systems. Progressive compression has been proposed to mitigate this problem, which offers data access with on-demand precision. However, existing approaches only consider precision control on raw data, leaving uncertainties on the quantities of interest (QoIs). We present a progressive data retrieval framework with guaranteed error control on derivable QoIs. Our contributions are three-fold. (1)We carefully derive the theories to control QoI errors during progressive retrieval. (2)We develop a general progressive retrieval framework based on the proposed theories, and optimize it by exploring feasible progressive representations. (3)We evaluate our framework using five real-world datasets with multiple QoIs. Experiments demonstrate that our framework can respect the QoI error bouds in the evaluated applications. This leads to over 2.02x performance gain in data transfer tasks compared to transferring the raw data while guaranteeing the QoI error.