Profiling data to determine metadata about a given dataset is an important and frequent
activity of any IT professional and researcher and is necessary for various use-cases. It …

Uncertainty often pervades information and knowledge. For this reason, the handling of
uncertainty in inference systems has been an issue for a long time in artificial intelligence …

We investigate the impact of uncertainty on relational data\-base schema design.
Uncertainty is modeled qualitatively by assigning to tuples a degree of possibility with which …

Driven by the dominance of the relational model and the requirements of modern
applications, we revisit the fundamental notion of a key in relational databases with NULL. In …

Data profiling is an enabler for efficient data management and effective analytics. The
discovery of data dependencies is at the core of data profiling. We conduct the first study on …

Classical data cleaning performs a minimal set of operations on the data to satisfy the given
integrity constraints. Often, this minimization is equivalent to vertex cover, for example when …

Inclusion dependencies form one of the most fundamental classes of integrity constraints.
Their importance in classical data management is reinforced by modern applications such …

Probabilistic databases address well the requirements of an increasing number of modern
applications that produce large volumes of uncertain data from a variety of sources …

Uncertainty, incompleteness, and imprecision are common characteristics of the data and
knowledge that we daily deal with in a wide range of domains and applications. Uncertainty …

This paper introduces possibilistic functional dependencies. These dependencies are
associated with a particular possibility distribution over possible worlds of a classical …