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

Abstract

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.

16 pages, 9 figures, 1 table

References in corpus (1)

• Managing large-scale scientific hypotheses as uncertain and probabilistic data

Cited by in corpus (1)

• A note on the complexity of the causal ordering problem