Anomaly detection and explanation in big volumes of real-world medical data, such as those
pertaining to COVID-19, pose some challenges. First, we are dealing with time-series data …

Data quality evaluation is built upon data quality measurement results.“Data quality
evaluation” uses the “data quality rules” representing the risk appetite of the organization to …

Data quality tests validate heterogeneous data to detect violations of syntactic and semantic
constraints. The specification of these constraints can be incomplete because domain …

Almost every company today is collecting and storing data for later analysis and business
decision making in some kind of repository, usually a data warehouse. In order for decision …

Data are published to encourage data exploitation. However, data quality issues threaten
data consumption and require data consumers investing time and effort in data cleansing …

Industrial control systems are target-rich environments for cyber criminals, terrorists and
advanced persistent threats. Researchers have investigated various types of industrial …

Industrial control systems (ICS) are an increasingly targetrich environment for cyber-
criminals, terrorists, and advanced persistent threats. Previously researchers have looked …

Data quality tests are used to validate the data stored in databases and data warehouses,
and to detect violations of syntactic and semantic constraints. Domain experts grapple with …

Power grids, gas pipelines, and manufacturing centers provide an interesting challenge for
cybersecurity research. Known as supervisory control and data acquisition systems …

The quality of data is extremely important for data analytics. Data quality tests typically
involve checking constraints specified by domain experts. Existing approaches detect trivial …