Managing large-scale scientific hypotheses as uncertain and probabilistic data with support for predictive analytics

摘要

The sheer scale of high-resolution raw data generated by simulation hasmotivated non-conventional approaches for data exploration referred as`immersive' and `in situ' query processing of the raw simulation data. Anotherstep towards supporting scientific progress is to enable data-driven hypothesismanagement and predictive analytics out of simulation results. We present asynthesis method and tool for encoding and managing competing hypotheses asuncertain data in a probabilistic database that can be conditioned in thepresence of observations.