Managing large-scale scientific hypotheses as uncertain and probabilistic data

In view of the paradigm shift that makes science ever more data-driven, in this thesis we propose a synthesis method for encoding and managing large-scale deterministic scientific hypotheses as uncertain and probabilistic data. In the form of mathematical equations, hypotheses symmetrically relate aspects of the studied phenomena. For computing predictions, however, deterministic hypotheses are used asymmetrically as functions. We build upon Simon's notion of structural equations in order to extract the (so-called) causal ordering embedded in a hypothesis structure (set of mathematical equations). We show how to process the hypothesis causal structure effectively through original algorithms for encoding it into a set of functional dependencies (fd's) and then perform causal reasoning in terms of acyclic pseudo-transitive reasoning over fd's. Such reasoning reveals important causal dependencies implicit in the hypothesis predictive data and guide our synthesis of U-relational databases. Like Graphical Models, such a probabilistic database should be normalized so that the uncertainty arisen from competing hypotheses is decomposed into factors and can be recovered with its joint probability distribution by a lossless join. This is motivated in our thesis as a design-theoretic principle for data-driven hypothesis management and predictive analytics. The method is applicable to both quantitative and qualitative deterministic hypotheses and demonstrated in realistic use cases from computational science.