On a possibilistic database model with incomplete possibility distributions
Patrick Bosc, Olivier Pivert · 2009
This paper proposes a possibilistic database model that enables dealing with incomplete possibility distributions. The situation considered is that where the data provider does not have a complete knowledge of the attribute domains involved and is only able to specify more or less possible and completely impossible candidates for some attribute values. It is shown that the framework we propose constitutes a strong representation system for the operations of selection (based on a restricted type of conditions), projection and union.