The vision of @math -DB introduces deterministic scientific hypotheses as a kind of uncertain and probabilistic data, and opens some key technical challenges for enabling data-driven hypothesis management and analytics. The @math -DB system addresses those challenges throughout a design-by-synthesis pipeline that defines its architecture. It processes hypotheses from their XML-based extraction to encoding as uncertain and probabilistic U-relational data, and eventually to their conditioning in the presence of observations. In this demo we present a first prototype of the @math -DB system. We showcase its core innovative features by means of use case scenarios in computational science in which the hypotheses are extracted from a model repository on the web and evaluated (rated ranked) as probabilistic data.
. The topic of conditioning a p-DB has been firstly addressed by Koch and Olteanu motivated by data cleaning applications @cite_8 . They have introduced the assert operation to implement, as in AI, a kind of knowledge compilation, viz., world elimination in face of constraints (e.g., FDs). For hypothesis management in @math -DB, nonetheless, there is a need to apply by asserting observed data (not constraints).
@cite_8
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As constraint logic programming matures and larger applications are built, an increased need arises for advanced development and debugging environments. Assertions are linguistic constructions which allow expressing properties of programs. Classical examples of assertions are type declarations. However, herein we are interested in supporting a more general setting [3, 1] in which, on one hand assertions can be of a more general nature, including properties which are statically undecidable, and, on the other, only a small number of assertions may be present in the program, i.e., the assertions are optional. In particular, we do not wish to limit the programming language or the language of assertions unnecessarily in order to make the assertions statically decidable. Consequently, the proposed framework needs to deal throughout with approximations [2].