Managing large-scale scientific hypotheses as uncertain and   probabilistic data with support for predictive analytics
  The sheer scale of high-resolution raw data generated by simulation has motivated non-conventional approaches for data exploration referred as `immersive' and `in situ' query processing of the raw simulation data. Another step towards supporting scientific progress is to enable data-driven hypothesis management and predictive analytics out of simulation results. We 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. 
Bernardo Gon\c{c}alves and Fabio Porto
cs.DB