  The vision of $\Upsilon$-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
$\Upsilon$-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
$\Upsilon$-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.
