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

Big Data technology has discarded traditional data modeling approaches as no longer
applicable to distributed data processing. It is, however, largely recognized that Big Data …

Data uncertainty is inherent in applications such as sensor monitoring systems, location-
based services, and biological databases. To manage this vast amount of imprecise …

MayBMS is a state-of-the-art probabilistic database management system which leverages
the strengths of previous database research for achieving scalability. As a proof of concept …

This demonstration showcases ProvSQL, an open-source module for the PostgreSQL
database management system that adds support for computation of provenance and …

Schema matching is a central challenge for data integration systems. Automated tools are
often uncertain about schema matchings they suggest, and this uncertainty is inherent since …

In graph applications (eg, biological and social networks), various analytics tasks (eg,
clustering and community search) are carried out to extract insight from large and complex …

The data handled in emerging applications like location-based services, sensor monitoring
systems, and data integration, are often inexact in nature. In this paper, we study the …

A database management system (DBMS) is a piece of software that makes it easier to
create, organize, store, retrieve, and manage structured data. It functions as a central system …

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 …