Uncertainty accompanies our life processes and covers almost all fields of scientific studies.
Two general categories of uncertainty, namely, aleatory uncertainty and epistemic …

Probabilistic databases are databases where the value of some attributes or the presence of
some records are uncertain and known only with some probability. Applications in many …

Probabilistic database systems have successfully established themselves as a tool for
managing uncertain data. However, much of the research in this area has focused on …

Probabilistic models involving relational and temporal aspects need exact and efficient
inference algorithms. Existing approaches are approximative, include unnecessary …

Most of the work on query evaluation in probabilistic databases has focused on the simple
tuple-independent data model, where tuples are independent random events. Several …

As more and more data is provided in RDF format, storing huge amounts of RDF data and
efficiently processing queries on such data is becoming increasingly important. The first part …

Information in many applications, such as mobile wireless systems, social networks, and
road networks, is captured by graphs. In many cases, such information is uncertain. We …

We address the issue of incorporating a particular yet expressive form of integrity constraints
(namely, denial constraints) into probabilistic databases. To this aim, we move away from …

Recent advances in Web-based information extraction have allowed for the automatic
construction of large, semantic knowledge bases, which are typically captured in RDF …

The Causal Effect (CE) is a numerical measure of causal influence of variables on observed
results. Despite being widely used in many areas, only preliminary attempts have been …