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(Credit to Varun Ratnakar and Yolanda Gil).
The automation of important aspects of scientific data analysis would significantly accelerate the pace of science and innovation. Although important aspects of data analysis can be automated, the hypothesize-test-evaluate discovery cycle is largely carried out by hand by researchers. This introduces a significant human bottleneck, which is inefficient and can lead to erroneous and incomplete explorations. We introduce a novel approach to automate the hypothesize-test-evaluate discovery cycle with an intelligent system that a scientist can task to test hypotheses of interest in a data repository. Our approach captures three types of data analytics knowledge: 1) common data analytic methods represented as semantic workflows; 2) meta-analysis methods that aggregate those results, represented as meta-workflows; and 3) data analysis strategies that specify for a type of hypothesis what data and methods to use, represented as lines of inquiry. Given a hypothesis specified by a scientist, appropriate lines of inquiry are triggered, which lead to retrieving relevant datasets, running relevant workflows on that data, and finally running meta-workflows on workflow results. The scientist is then presented with a level of confidence on the initial hypothesis (or a revised hypothesis) based on the data and methods applied. We have implemented this approach in the DISK system, and applied it to multi-omics data analysis.