Managing Scientific Hypotheses as Data with Support for Predictive Analytics
The sheer scale of high-resolution raw data generated by simulation has motivated nonconventional approaches for data exploration, referred to as immersive and in situ query processing. Another step toward supporting scientific progress is to enable data-driven hypothesis management and predictive analytics out of simulation results. The authors of this article present a synthesis method and tool for encoding and managing competing hypotheses as uncertain data in a probabilistic database that can be conditioned in the presence of observations.